JP2015040864A - Symbol string conversion method, speech recognition method, device therefor, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for converting an N-gram model and backoff N-gram model into WFST faster than possible with prior art and using the converted WFST in symbol string conversion.SOLUTION: A symbol string conversion method according to the present invention includes: a state transition set acquisition step of referencing, using state information which is the information for specifying a state reached by a symbol in an input symbol string and each hypothesis in a present set of hypotheses as input, a symbol string model storage unit in which the occurrence probability or chain probability of a symbol string comprising one or more symbols is stored as a symbol string model and acquiring a state transition transitable from a transition source, the transition source being a state reached by the present hypothesis, and a state transition probability thereof; and a hypothesis development step of updating each hypothesis in the present set of hypotheses by using a set of state transitions transitable from the transition source as obtained in the state transition set acquisition step.

Description

  The present invention is given a certain input symbol sequence while referring to a weighted finite state transducer (hereinafter also referred to as “WFST”) representing a symbol sequence conversion rule for converting a symbol sequence. Sometimes, a symbol string conversion technique for searching a state transition process having a minimum or maximum cumulative weight from various state transition processes of the WFST and outputting an output symbol string of the state transition process as a conversion result, and The present invention relates to a speech recognition technique in which an input symbol string is a series of feature vectors representing a speech signal and an output symbol string is a word string.

  WFST is a method of expressing rules for converting a symbol string into another symbol string in a state and state transition diagram.

  Conventionally, in speech recognition, a symbol string (sequence of feature vectors) representing an acoustic pattern of input speech is input, and a WFST that outputs a word string corresponding to the acoustic pattern is represented by a back-off N-gram model. A method for obtaining a word string (speech recognition result) that is acoustically and linguistically appropriate by synthesizing a WFST of a language model and performing symbol string conversion is known (Non-patent Document 1 and Patent Document). 1).

The N-gram model as a language model is a model that expresses the ease of word concatenation by an N-1 Markov process. If | V | is the number of registered words, | V | ^N probability values have. If expressed in WFST, | V | ^N state transitions are required. Since such WFST is unrealistic, backoff N-gram model is used in speech recognition by WFST. The back-off N-gram model is a type of N-gram model that uses the N-1 gram probability estimated more accurately when the N-gram probability cannot be estimated accurately due to lack of learning data, etc. is there. For example, when the N-gram probability becomes 0, the N-1 gram probability is returned, and when the N-1 gram probability becomes 0, the N-2 gram probability is backed off (retreated).

JP 2005-257910 A

Hori, Tsukada, “Speech recognition by weighted finite state transducer”, Journal of Information Processing Society of Japan, Vol. 45, No. 10, Oct. 2004.

  In Non-Patent Document 1 and Patent Document 1, when using a back-off N-gram for symbol string conversion by WFST, it is necessary to convert the back-off N-gram model into WFST in advance. Then, an arbitrary input symbol string is efficiently converted using the WFST. However, every time the backoff N-gram model is modified, it must be converted back to WFST. Therefore, a certain processing time is required to convert the back-off N-gram model into WFST in advance. For example, in speech recognition, the N-gram probability of a language model may be adjusted so that a word on a specific topic can be recognized well. In this case, even if the adjustment is correction of some probabilities, the entire language model must be converted back to WFST before performing speech recognition. This results in a loss of immediacy when actually using speech recognition or the like.

  In some cases, the probability value of the language model may be dynamically changed. For example, when using speech recognition, when it is found that a word related to a specific topic is likely to be spoken, the information is immediately reflected in the speech recognition system.

  When such a dynamic language model is used, the calculation cost reflected in the weight of WFST is large every time the probability value is changed. In addition, when reflecting in the weight of WFST, it is necessary to recalculate not only the changed probability but also the back-off coefficient. Since speech recognition uses language models with millions to tens of millions of N-gram probabilities, this calculation is very large, and even if the language model is updated dynamically, speech recognition can be started immediately. There is no problem.

  A method for instantaneously converting the N-gram model and the back-off N-gram model to WFST and using it for symbol string conversion is not known.

  An object of the present invention is to provide a method of converting an N-gram model and a back-off N-gram model into WFST earlier than the prior art and using it for symbol string conversion.

  In order to solve the above problems, according to the first aspect of the present invention, a symbol string conversion method input symbol string is converted into an output symbol string. The symbol string conversion method includes a symbol string acquisition step for acquiring symbols one by one from the top of the input symbol string, and a state in which each hypothesis of the symbol acquired in the symbol string acquisition step and the current hypothesis set has been reached. With reference to state information that is information to be identified, the current hypothesis is determined by referring to a symbol string model storage unit in which the occurrence probability or the chain probability of a symbol string composed of one or more symbols is stored as a symbol string model. State transition set acquisition step that acquires the state transition that can be transitioned from the transition source and its state transition probability, and state transition that can be transitioned from the transition source acquired in the state transition set acquisition step Using the set of hypotheses, the hypothesis expansion step that updates each hypothesis in the current hypothesis set, and the hypothesis that has reached the same state in the hypothesis set updated in the hypothesis expansion step. If hypotheses are narrowed down by deleting hypotheses other than a predetermined number of hypotheses from the one with the largest or smallest weight, and if the input symbol string has been read to the end in the symbol string acquisition step, the hypothesis that has reached the end state A hypothesis narrowing step for obtaining a hypothesis having a maximum or minimum cumulative weight as an output symbol string.

  In order to solve the above problems, according to another aspect of the present invention, a speech recognition method converts an acoustic feature symbol string obtained from speech into speech recognition results. The speech recognition method refers to a word string WFST that uses a symbol string acquisition step of acquiring symbols one by one in order from the beginning of the acoustic feature symbol string, and a score of the acoustic feature symbols calculated by the acoustic model as a weight, A hypothesis expansion step for adding a new state transition to each of the current hypothesis set, a symbol string output from each state transition process of the hypothesis set to which the state transition was added in the hypothesis expansion step, and a current hypothesis Language model storage in which the occurrence probability or chain probability of a symbol string consisting of one or more symbols is stored as a symbol string model, with the state information that is information specifying the state reached by each hypothesis of the set being input The state transition set acquisition step that acquires the state transition that can be transitioned from the transition source and the state transition probability, with the state that the current hypothesis has reached as a transition source. And a hypothesis correction step that updates each hypothesis in the current hypothesis set using a set of state transitions that can be transitioned from the transition source acquired in the state transition set acquisition step, and a hypothesis updated in the hypothesis expansion step. The hypothesis is narrowed down by deleting hypotheses other than a predetermined number of hypotheses from the hypothesis that has the maximum or minimum cumulative weight among the hypotheses that have reached the same state in the set. A hypothesis narrowing step for obtaining a hypothesis having the maximum or minimum cumulative weight as a speech recognition result from the hypotheses that have reached the end state.

  In order to solve the above problems, according to another aspect of the present invention, a symbol string conversion device converts an input symbol string into an output symbol string. The symbol string converter includes a symbol string acquisition unit that acquires symbols one by one from the top of the input symbol string, and a state in which each hypothesis of the symbol acquired by the symbol string acquisition unit and the current hypothesis set has been reached. With reference to state information that is information to be identified, the current hypothesis is determined by referring to a symbol string model storage unit in which the occurrence probability or the chain probability of a symbol string composed of one or more symbols is stored as a symbol string model. State transition set acquisition unit that acquires the state transition that can be transitioned from the transition source and its state transition probability, and state transition that can be transitioned from the transition source acquired by the state transition set acquisition unit The hypothesis expander that updates each hypothesis in the current hypothesis set using the set of hypotheses, and the hypothesis that has reached the same state among the hypotheses updated in the hypothesis expander, the cumulative weight is the maximum or minimum From what becomes If the hypothesis is narrowed down by deleting hypotheses other than the constant hypothesis and the input symbol string is read to the end in the symbol string acquisition unit, the cumulative weight is the maximum or minimum among the hypotheses that have reached the end state. And a hypothesis narrowing-down unit that obtains a hypothesis as an output symbol string.

  In order to solve the above problems, according to another aspect of the present invention, a speech recognition apparatus converts an acoustic feature symbol string obtained from speech into speech recognition results. The speech recognition apparatus refers to a word string WFST that uses a symbol string acquisition unit that acquires symbols one by one in order from the beginning of the acoustic feature symbol string, and a score of the acoustic feature symbol calculated by the acoustic model as a weight, A hypothesis expander that adds a new state transition to each of the current hypothesis set, a symbol string output from each state transition process of the hypothesis set that was added a state transition in the hypothesis expander, and a current hypothesis Language model storage in which the occurrence probability or chain probability of a symbol string consisting of one or more symbols is stored as a symbol string model, with the state information that is information specifying the state reached by each hypothesis of the set being input A state transition set acquisition unit that acquires a state transition that can be transitioned from the transition source and the state transition probability, and a state transition set acquisition. Using the set of state transitions that can be transitioned from the transition source acquired in the section, the hypothesis correction unit that updates each hypothesis in the current hypothesis set and the hypothesis expansion unit that has reached the same state If the hypothesis is narrowed down by deleting hypotheses other than a predetermined number of hypotheses from the hypothesis that has the largest or smallest cumulative weight, the process ends if the input symbol string is read to the end in the symbol string acquisition unit A hypothesis narrowing-down unit that obtains a hypothesis having a maximum or minimum cumulative weight as a speech recognition result among hypotheses that have reached a state.

  According to the present invention, the N-gram model and the back-off N-gram model can be converted to WFST earlier than the prior art and used for symbol string conversion.

The figure which shows an example of WFST. The figure which shows WFST with a table | surface. The figure which shows an example of the symbol string conversion using one WFST. The figure which shows the symbol sequence conversion procedure using WFST. The figure which shows the example of the state of a symbol string trigram, and a state transition. The figure which shows the example of the state showing the back-off to the bigram of symbol sequence trigram, and a state transition. The figure showing the example showing the backoff to the unigram of a symbol string trigram, and a state transition. The functional block diagram of the symbol string conversion apparatus which concerns on 1st embodiment. The figure which shows an example of the processing flow of the state transition set acquisition means 809. FIG. The figure which shows the example showing the state which represents the back-off to the bigram of a symbol sequence trigram, and a state transition in the symbol sequence converter which concerns on 1st embodiment. The figure which shows the example showing the state which represents the back-off to the unigram of a symbol string trigram, and a state transition in the symbol string converter which concerns on 1st embodiment. The figure which shows an example of the data structure of a symbol sequence back-off trigram. The functional block diagram of the speech recognition apparatus which concerns on 2nd embodiment.

Hereinafter, embodiments of the present invention will be described. In the drawings used for the following description, constituent parts having the same function and steps for performing the same process are denoted by the same reference numerals, and redundant description is omitted. In the following explanation, the symbols “^”, “~”, “ ⁻ ”, etc. used in the text should be written directly above the previous character. Immediately after. In the formula, these symbols are written in their original positions. Further, the processing performed for each element of a vector or matrix is applied to all elements of the vector or matrix unless otherwise specified.

<Points of first embodiment>
The WFST of the backoff N-gram model that represents the symbol string model is not constructed in advance, and the input symbol string is given, and in the process of converting the symbol string, the necessary state number and state transition are backoff N-grams when necessary. Convert the model to WFST and take it out.

  That is, initial state acquisition means for acquiring the WFST initial state number from the back-off N-gram model, and when an arbitrary state number and input symbol of WFST are given, the state corresponding to the given state number is displayed. State transition set acquisition means for acquiring a set of state transitions as a transition source, and when given any state number of WFST, the given state number (also referred to as state information in the sense of state specifying information) ) Prepares an end state determination means for determining whether the state corresponding to WFST is an end state of WFST, and in order to refer to the WFST of the symbol string model at the time of symbol string conversion, obtain an initial state acquisition means and a state transition set acquisition By calling the means and the end state determining means, only the necessary state transition of the WFST of the symbol string model is generated in response to the reference request, and the symbol string is converted using the set of the generated state transitions.

<Prerequisite knowledge>
Prior to describing the first embodiment, the prerequisite knowledge will be described.
WFST consists of (1) state, (2) state transition indicating that transition from state to state is possible, (3) input symbol accepted in state transition, (4) output symbol output at that time, and (5) It is defined by a set of weights for the state transition. WFST is a model that, when a certain input symbol string is given, repeats the state transition while outputting output symbols according to the state transition that sequentially accepts the symbols of the input symbol sequence from the initial state, and terminates when the end state is reached. is there. Formally, WFST is defined by the following eight sets (Q, Σ, Δ, i, F, E, λ, ρ).
1. Q is a set of finite states.
2. Σ is a finite set of input symbols.
3. Δ is a finite set of output symbols.
4). iεQ is the initial state.
5. FεQ is a set of end states.
6). EεQ × Σ × Δ × Q is a set of state transitions in which an output symbol is output from the current state according to an input symbol to transition to the next state.
7). λ is the initial weight.
8). ρ (q) is the end weight of the end state q. qεF.

An example of WFST is shown in FIG.
In FIG. 1, 10 indicates a state represented by a circle (“◯”), and the number in the circle represents the state number. 11 indicates an end state represented by a double circle (“◎”), and the number in the double circle is the number of the end state and the end accumulated at the end of the state transition. The weight is expressed as “(state number) / (end weight)”. Hereinafter, when the state is indicated using the state number, the state and the number are simply referred to as “state 0” or “state 3”. Reference numeral 12 denotes a state transition represented by an arrow (“→”) connecting each state, and a symbol or a number given to each state transition is an input symbol, an output symbol, The weight is expressed as “(input symbol) :( output symbol) / (weight)”.

  As shown in FIG. 2, the WFST of FIG. 1 can be defined by a table. In FIG. 2, each row represents one state transition, and the state number of the transition source (current state) and the state number of the transition destination (next state), the input symbol, the output symbol, and the weight in the state transition are described. In the final state (state 3 in FIG. 1), the transition destination, the input symbol, and the output symbol are empty, and the weight (end weight) accumulated at the end of the state transition is described. In general, the initial state of WFST is set to state 0, and the initial weight λ is often omitted. Therefore, in this embodiment, the initial state is set to state 0, the initial weight is omitted, and it is not specified.

  The WFST of FIG. 1 can convert, for example, input symbol strings a, a, b, and c into output symbol strings d, d, c, and b, and the state transition process at this time uses a sequence of state numbers. In other words, 0, 0, 1, 3 and the cumulative value of weight (hereinafter referred to as “cumulative weight”) is 0.5 + 0.5 + 0.3 + 1 + 0.5 = 2.8. However, in the WFST of FIG. 1, two state transition processes of 0, 0, 1, 3 and 0, 0, 2, 3 can be considered for input symbol strings a, a, b, and c. In general, when there is a possibility of multiple state transitions for an input symbol string (this is called non-determinism), the state transition process in which the cumulative weight in the state transition process is minimized or maximized is selected, and the cumulative weight is selected. Selects the output symbol string corresponding to the state transition process with the minimum or maximum. When setting a higher weight for the one with the high possibility of state transition, select the output symbol string corresponding to the state transition process with the largest cumulative weight, and for the one with the high possibility of state transition. When a smaller weight is set, an output symbol string corresponding to the state transition process with the smallest cumulative weight may be selected. Also in the example of FIG. 1, the state transition processes 0, 0, 1, 3 having the smallest cumulative weight are selected for the input symbol strings a, a, b, c, and the conversion results are d, d, c, b. And

ここで、Ｗ（Ｘ→Ｙ；Ａ）は、重み付き有限状態変換器Ａによって記号列Ｘが記号列Ｙに変換されるときの状態遷移過程における累積重みを表す。この累積重みＷ（Ｘ→Ｙ；Ａ）の最小値Ｗ（Ｘ）を求めて、その最小値を与える記号列Ｙが記号列変換結果となる。この記号列変換結果を求めるには、入力記号列によって初期状態から終了状態に至るコスト（累積重み）が、最小または最大の状態遷移過程を探し出すことによって行われる。この手順は、例えば、特許文献１に開示されている。 When there is a weighted finite state converter A and a symbol string X is given as an input symbol string to the weighted finite state converter A, an output symbol string (that is, a symbol string conversion result that minimizes the accumulated weight) ) Is required to calculate the minimum value W (X) of the next cumulative weight.

Here, W (X → Y; A) represents the cumulative weight in the state transition process when the symbol string X is converted to the symbol string Y by the weighted finite state converter A. The minimum value W (X) of the cumulative weight W (X → Y; A) is obtained, and the symbol string Y giving the minimum value is the symbol string conversion result. The symbol string conversion result is obtained by searching for a state transition process in which the cost (cumulative weight) from the initial state to the end state is minimized or maximized by the input symbol string. This procedure is disclosed in Patent Document 1, for example.

An example of symbol string conversion using one WFST is shown in FIG.
First, in this specification, “hypothesis” means that symbols constituting a certain symbol string are sequentially input (read), and the input symbol string read up to the present time is input from the initial state in WFST. It is assumed that one state transition process that may occur when the state transition is repeated by a sequence is represented.

  The symbol string acquisition unit 103 reads (acquires) the symbols constituting the input symbol string one by one from the top in order, and sends them to the hypothesis development unit 104.

  The hypothesis expansion unit 104 uses the symbols newly received as a set of hypotheses for the symbol strings read so far, according to the symbols acquired by the symbol string acquisition unit 103 and the WFSTs read from the WFST storage unit 101, so that the state transition of each hypothesis By updating the process, a new hypothesis is generated and sent to the hypothesis narrowing unit 105.

  The hypothesis narrowing unit 105 narrows down the hypotheses by deleting hypotheses other than the hypothesis having the smallest or largest cumulative weight among the hypotheses reaching the same state from the hypothesis set received from the hypothesis developing unit 104. If the input symbol string has been read to the end, the hypothesis narrowing-down unit 105 sends the output symbol string corresponding to the hypothesis having the minimum or maximum cumulative weight to the symbol string output unit 106. If the input symbol string has not been read to the end, the hypothesis is sent to the hypothesis developing unit 104.

  The symbol string output unit 106 outputs the output symbol string received from the hypothesis narrowing unit 105 as a symbol string conversion result.

Next, an example of a procedure for converting a symbol string based on this embodiment will be described.
First, when a state transition in WFST is expressed as e, n [e] is a transition destination state (next state), i [e] is an input symbol, o [e] is an output symbol, and w [e] is a weight. Define. Also, when a certain hypothesis is expressed as h, s [h] is a state reached in the state transition process, W [h] is an accumulated weight in the state transition process, and O [h] is output in the state transition process. It is a symbol string.

  In this procedure, hypotheses are managed using a list of hypotheses (hereinafter referred to as “hypothesis list”). Hypotheses can be inserted into and extracted from the hypothesis list. However, when a hypothesis is inserted into the hypothesis list, if there is a hypothesis that has reached the same state in the hypothesis list, only the smaller or larger cumulative weight is left in the hypothesis list to narrow down the hypotheses.

The symbol string conversion procedure using WFST is shown in FIG.
In the following, the procedure performed by each unit of the example of symbol string conversion using WFST (FIG. 3) will be described with reference to FIG.

  Starting from step S101, as an initial setting, empty hypothesis lists H and H 'are generated in step S102. In step S103, an initial hypothesis h (h represents a hypothesis before being updated in the hypothesis developing unit 104) is generated, a state s [h] = 0 (an initial state of WFST), and a cumulative weight W [h] = 0. The output symbol string O [h] = φ (here, φ represents an empty symbol string) and is inserted into the hypothesis list H.

  In step S104, the symbol string acquisition unit 103 reads one symbol included in the input symbol string, substitutes the symbol for x, and outputs the symbol to the hypothesis expansion unit 104. The following steps S105 to S108 are executed in the hypothesis developing unit 104.

  In step S105, one hypothesis is extracted from the hypothesis list H and substituted into the hypothesis h, and a state transition list (hereinafter also referred to as “state transition list”) E whose input symbol is equal to x is prepared from the state s [h]. .

  In step S106, if the state transition list E = φ (here, φ represents an empty list), the process proceeds to S110. Otherwise, the process proceeds to S107, and one state transition is extracted from the state transition list E and substituted into e.

  In step S108, a new hypothesis f (f represents a hypothesis after being updated in the hypothesis developing unit 104) is generated, and a state s [f] = n [e] and a cumulative weight W [f] = W [h] + w [e], output symbol string O [f] = O [h] · o [e], and output to hypothesis narrowing down section 105. Here, “·” represents an operation of connecting two symbols or symbol strings to form one symbol string.

  Step S109 is executed by the hypothesis narrowing-down unit 105 and narrows down the hypotheses by inserting the hypothesis f into the hypothesis list H ′. For example, if there is a hypothesis that reaches the same state in the hypothesis list H ′, only the smaller or larger cumulative weight is left in the hypothesis list H ′ to narrow down the hypotheses.

Returning from step S109 to S106, a hypothesis is developed for the next state transition.
In step S110, if the hypothesis list H = φ (all hypotheses have been expanded), the process proceeds to S111. Otherwise, return to S106 and develop the next hypothesis h.
In step S111, all the elements of the newly generated hypothesis list H ′ are transferred to the already hypothesized hypothesis list H, and the process proceeds to S112.

In step S112, if there is a next input symbol in the symbol string acquisition unit 103, the process returns to S104. Otherwise, it is determined that all the input symbol strings have been read, and the process proceeds to S113.
In step S113, after adding the end weight of the end state to the cumulative weight of the hypothesis reaching the end state in the hypothesis list H, the cumulative weight (W The hypothesis h that minimizes [h]) is selected, and the symbol string output unit 106 outputs the symbol string conversion result O [h] as the symbol string conversion result.
In step S114, the symbol string conversion procedure using WFST is terminated.

[Example of changing symbol string]
The process of obtaining the output symbol string when the input symbol strings a, a, b, and c are given to the WFST of FIG. 1 according to this symbol string conversion procedure will be described step by step. However, here, if there is a hypothesis of the current state number s, the output symbol string O, and the cumulative weight W, the hypothesis is represented as (s, O, W). A state transition (current state number s, next state number n, input symbol x, output symbol y, weight w) of WFST is represented as <s → n, x: y / w>. In this example, an output symbol string corresponding to a state transition process with the smallest cumulative weight is selected as a symbol string conversion result.

Starting from S101, empty hypothesis lists H and H ′ are created in S102.
A hypothesis (0, φ, 0) in the hypothesis list H is inserted in S103.

(Read symbol “a”)
In S104, the symbol a is read and set as x. In S105, a hypothesis (0, φ, 0) is extracted from the hypothesis list H. A state transition list E including a state transition <0 → 0, a: d / 0.5> whose input symbol is equal to a is created from the current state 0 of this hypothesis (see FIG. 1).
Since the state transition list E is not equal to φ in S106, the process proceeds to S107, where the state transition <0 → 0, a: d / 0.5> is extracted, and a new hypothesis (0, d, 0.5) is generated in S108. , S109 is inserted into the hypothesis list H ′.
Returning to S106, since the state transition list E = φ, the process proceeds to S110, and since the hypothesis list H = φ, the process proceeds to S111. The element (0, d, 0.5) of the hypothesis list H ′ is moved to the hypothesis list H. Since the next input symbol exists in S112, the process returns to S104.

In step S104, the symbol a is read and set as x. In S105, a hypothesis (0, d, 0.5) is extracted from the hypothesis list H. A state transition list E including a state transition <0 → 0, a: d / 0.5> whose input symbol is equal to a is generated from the current state 0 of this hypothesis.
Since E = φ is not satisfied in S106, the process proceeds to S107, and the state transition <0 → 0, a: d / 0.5> is extracted from the state transition list E. A new hypothesis (0, dd, 1) is generated in S108, and is inserted into the hypothesis list H ′ in S109.
Returning to S106, since the state transition list E = φ, the process proceeds to S110, and since the hypothesis list H = φ, the process proceeds to S111. The element (0, dd, 1) of the hypothesis list H ′ is moved to the hypothesis list H, and since the next input symbol exists in S112, the process returns to S104.

(Read symbol “b”)
In step S104, the symbol b is read and set as x. In S105, a hypothesis (0, dd, 1) is extracted from the hypothesis list H. A state transition list E including state transitions <0 → 1, b: c / 0.3> and <0 → 2, b: b / 1> whose input symbol is equal to b is created from the current state 0 of this hypothesis.
Since the state transition list E is not equal to φ in S106, the process proceeds to S107, and the first state transition <0 → 1, b: c / 0.3> is extracted from the state transition list E. A new hypothesis (1, ddc, 1.3) is generated in S108, and inserted into the hypothesis list H ′ in S109.
Returning to S106, since the state transition list E is not equal to φ, the process proceeds to S107, and the second state transition <0 → 2, b: b / 1> is extracted from the state transition list E. A new hypothesis (2, ddb, 2) is generated at S108, and inserted into the hypothesis list H ′ at S109.
Returning to S106, since the state transition list E = φ, the process proceeds to S110, and since the hypothesis list H = φ, the process proceeds to S111, and the elements (1, ddc, 1.3) and (2, ddb, 2) of the hypothesis list H ′. ) Is moved to the hypothesis list H, and since the next input symbol exists in S112, the process returns to S104.

(Read symbol “c”)
In step S104, the symbol c is read and set as x. In S105, the first hypothesis (1, ddc, 1.3) is extracted from the hypothesis list H. A state transition list E including a state transition <1 → 3, c: b / 1> whose input symbol is equal to c is created from the current state 1 of this hypothesis.
Since E = φ is not satisfied in S106, the process proceeds to S107, and the state transition <1 → 3, c: b / 1> is extracted from the state transition list E. A new hypothesis (1, ddcb, 2.3) is generated in S108, and is inserted into the hypothesis list H ′ in S109.

Returning to S106, since the state transition list E = φ, the process proceeds to S110, and since the hypothesis list H ≠ φ, the process returns to S105 to extract the second hypothesis (2, ddb, 2) from the hypothesis list H. A state transition list E including a state transition <2 → 3, c: a / 0.6> whose input symbol is equal to c is created from the current state 2 of this hypothesis.
Since the state transition list E is not equal to φ in S106, the process proceeds to S107, and the state transition <2 → 3, c: a / 0.6> is extracted from the state transition list E. A new hypothesis (3, ddba, 2.6) is generated at S108, and inserted into the hypothesis list H ′ at S109. At this time, the hypothesis list H ′ already includes the hypothesis (3, ddcb, 2.3), and the hypothesis (3, ddba, 2.6) has reached the same state 3, so The hypothesis (3, ddcb, 2.3) having a small weight is left, and the hypothesis (3, ddba, 2.6) is deleted from the hypothesis list H ′.

Returning to S106, since the state transition list E = φ, the process proceeds to S110, and since the hypothesis list H = φ, the process proceeds to S111. In S111, the element (3, ddcb, 2.3) of the hypothesis list H ′ is moved to the hypothesis list H. In S112, since there is no next input symbol, the process proceeds to S113.
In S113, since the arrival state 3 of the hypothesis (3, ddcb, 2.3) in the hypothesis list H is an end state, the end weight is added to (3, ddcb, 2.8), and this hypothesis is an end state Is the only hypothesis that has been reached, and since the accumulated weight is the smallest, the output symbol string ddcb is output as a conversion result, and the symbol string conversion process is terminated in S114.

(N-gram model)
On the other hand, there is a method of converting an N-gram model for calculating the N chain probability of a symbol string into WFST and using it for symbol string conversion.
First, when there is a symbol string w ₁ , w ₂ ,..., W _L , this is represented as w ₁ ^L. The symbol string w _j from the j-th to k-th, ..., a w _k represents the w _j ^k, especially ^{_{w j k = w j = w}} k is the time of _{j = k, j> w j} k when the k = ε. Note that ε is a symbol indicating that there is no nothing and nothing.

The appearance probability P (w ₁ ^L ) of the symbol string w ₁ ^L can be calculated as a product of occurrence probabilities of individual symbols as in the following equation.

ここで、P(w_i│w_i-N+1 ^i-1)は記号w_iが直前のＮ−１個の記号列w_i-N+1 ^i-1に依存して生起するNグラム確率を表す。特に、Ｎ＝１のときをユニグラムモデル、Ｎ＝２のときをバイグラムモデル、Ｎ＝３のときをトライグラムモデルと呼び、P(w_i|ε)、P(w_i│w_i-1)、P(w_i│w_i-2 ^i-1)をそれぞれユニグラム確率、バイグラム確率、トライグラム確率と呼ぶ。なお、ユニグラム確率はある記号w_iが生起する確率ともいえる。 A model that approximates that the appearance probability of each symbol depends only on the immediately preceding N−1 symbol string is the next N-gram model.

_{Here, P (w i │w i-} N + 1 i-1) is the N-gram probability that the symbol w _i is to occur, depending on the N-1 symbol string ^{_{w i-N + 1 i-}} 1 of the previous Represents. In particular, when N = 1, it is called a unigram model, when N = 2, a bigram model, and when N = 3, it is called a trigram model. P (w _i | ε), P (w _i | w _i-1 ) And P (w _i | w _{i −2} ⁱ⁻¹ ) are called unigram probability, bigram probability, and trigram probability, respectively. The unigram probability can be said to be a probability that a certain symbol w _i occurs.

For example, when the symbol string is a natural language, each symbol represents a word and the symbol string represents a sentence. At this time, whether or not a certain word string seems to be a sentence can be calculated as P (w ₁ ^L ) by the N-gram model.
In order to estimate individual N-gram probabilities, first, a large amount of symbol string data (for example, a newspaper article corpus) is prepared, and then the linkage statistics of the symbol strings are obtained in advance.

のように求めることができる。ここでC(abc)およびC(ab)は、それぞれ記号列データ内での記号列abcおよび記号列abの出現回数を表している。 For example, to estimate the trigram probability of a symbol string ab followed by a symbol c from some symbol string data

Can be obtained as follows. Here, C (abc) and C (ab) represent the number of appearances of the symbol string abc and the symbol string ab, respectively, in the symbol string data.

  However, since the amount of symbol string data is finite, for symbol strings consisting of N symbols that did not appear in the symbol string data by chance even though it is valid as a sequence of symbol strings. There is a problem that the N-gram probability becomes zero (zero frequency problem).

(Back-off N-gram model)
Therefore, in order to prevent the N-gram probability from becoming zero, when the N-gram probability of a symbol string composed of N symbols is zero, the N-1 gram probability is used, and further, the N-1 gram probability is zero. In such a case, a method of back-off (retreat) such as N-2 gram probability is used. Such an N-gram model is called a back-off N-gram model. However, even without the frequency of symbol columns 0, if extremely small (e.g., with respect to symbol string types 10,000 ^3, when the frequency is 1 or 2) is the probability value estimated by equation (4) Since the reliability is lowered, the frequency of the symbol string is deliberately assumed to be 0 (this is referred to as cut-off), and the back-off may be forcibly performed.

ここで、P^*(w_i│w_i-N+1 ^i-1)は、式(4)のように頻度から推定されるNグラム確率を少し小さくした値である。このように確率を小さく見積もることをディスカウントと呼ぶ。本来、確率が０であるNグラムに対してバックオフによるN-1グラム確率を割り当てると、確率の総和が１を超えてしまう。そこで、確率の総和を１にするため、Nグラム確率をディスカウントし、ディスカウントによって生じた確率の残りをバックオフ用に割り当てる。そして、α(w_i-N+1 ^i-1)はバックオフする際にN-1グラム確率に掛けるバックオフ係数である。ディスカウントされた確率とバックオフ係数を求める方法として、Good-Turing法、Witten-Bell法、Kneser-Ney法などが知られている。 The backoff N-gram probability that w _i occurs after the symbol sequence w _{i-N + 1} ⁱ⁻¹ is calculated as follows.

Here, P ^* (w _i | w _{i −N + 1} ⁱ⁻¹ ) is a value obtained by slightly reducing the N-gram probability estimated from the frequency as shown in Equation (4). Such estimation of the probability as small is called discount. If the N-1 gram probability by back-off is assigned to the N gram with a probability of 0, the sum of the probabilities exceeds 1. Therefore, in order to set the sum of probabilities to 1, the N-gram probability is discounted, and the remaining probability generated by the discount is allocated for backoff. Α (w _{i−N + 1} ⁱ⁻¹ ) is a back-off coefficient to be multiplied by the N−1 gram probability at the time of back-off. Good-Turing method, Witten-Bell method, Kneser-Ney method, etc. are known as methods for obtaining discounted probability and backoff coefficient.

分子は記号列w₁ ^mの後に続くあらゆる記号vについてディスカウントされたNグラム確率P^*(v│w₁ ^m)の総和を求め、１から減算した値であり、これはディスカウントによってバックオフのために確保された確率を表す。一方、分母は、バックオフしたときに使われるN-1グラム確率P^*(v│w₂ ^m)の総和であり、１−（バックオフで使われないN-1グラム確率の総和）として計算されている。つまり、バックオフ係数α(w₁ ^m)は、バックオフによって確保された確率を、バックオフに使われるN-1グラムで、その元々のN-1グラム確率に比例した割合で分け合うための係数である。バックオフ係数をこのように求めることで、バックオフNグラム確率は、確率の性質（０以上１以下、総和が１）を保証している。 The back-off coefficient α (w ₁ ^m ) for the symbol string w ₁ ,..., W _m is calculated as follows. However, m = N-1 is assumed.

The numerator is the sum of the discounted N-gram probabilities P ^* (v│w ₁ ^m ) for any symbol v following the symbol string w ₁ ^m , and is the value subtracted from 1 because this is a back-off due to the discount Represents the probability secured in. On the other hand, the denominator is the sum of N-1 gram probabilities P ^* (v│w ₂ ^m ) used when backoff, and is calculated as 1- (sum of N-1 gram probabilities not used in backoff) Has been. In other words, the back-off coefficient α (w ₁ ^m ) is a coefficient for sharing the probability secured by back-off with the N-1 gram used for back-off in proportion to the original N-1 gram probability. It is. By obtaining the back-off coefficient in this way, the back-off N-gram probability guarantees the nature of the probability (0 to 1 and the sum is 1).

  When an N-gram model is handled by a computer, it is necessary to store a permutation of N symbols and its probability value. However, as the number of types of symbols increases, the number of types of permutations increases exponentially with respect to N. For example, when the number of types of symbols is 10,000, the number of types of trigrams is 10,000 cubed. This is too large and requires a huge storage capacity when handled by a computer. However, since there are far fewer permutations that actually appear in the symbol string data, most N-gram probabilities are zero. Therefore, it is only necessary to hold permutations with N-gram probabilities greater than 0 and their N-gram probabilities. Further, in the back-off N-gram model, a permutation (symbol string) composed of 1 to N symbols having a probability greater than 0, its probability, and a back-off coefficient may be held.

(How to convert backoff N-gram model to WFST)
Next, a method for converting the back-off N-gram model into WFST is shown.
Basically, each permutation consisting of N-1 symbols held by the backoff N-gram model is assigned to one state of WFST, and each state transition occurs next to the permutation consisting of N-1 symbols. Symbols that are likely to be input are input symbols and output symbols, and their N-gram probabilities (logarithm negative values) are used as weights and connected to corresponding transition destination states. For example, in the trigram model, the state transition from the state corresponding to the symbol string ab to the state corresponding to the symbol string bc is c for the input symbol and output symbol and -logP ^* (c | ab) for the weight (FIG. 5). reference).

If there is no permutation of N symbols, that is, if the N-gram probability is 0, a state transition for backoff is prepared, and a weight of a backoff coefficient (logarithm negative value) is assigned, A transition is made to a state corresponding to a permutation composed of N-2 symbols, which is shortened by one symbol string. For example, if there is no permutation (symbol string) abc of three symbols in the trigram model, that is, P ^* (c | ab) = 0, a state transition for backoff is created. This transitions from the state corresponding to the symbol string ab, which is the transition source, to the state corresponding to the symbol b with the weight -logα (ab). Then, from the state corresponding to the symbol b to the state corresponding to the symbol string bc, the state transition having the input symbol and the output symbol c and the weight as the bigram probability (logarithm negative value) -logP ^* (c | b) Make (see Fig. 6). As a result, the WFST is constructed in such a manner that the calculation α (ab) P ^* (c | b) based on the back-off in Expression (5) is embedded in the state transition. Furthermore, if there is no permutation bc consisting of two symbols, that is, P ^* (c | b) = 0, the weight -logα (b) from the state corresponding to the symbol b to the state corresponding to the symbol ε Create a state transition for Then, from the state corresponding to the symbol ε to the state corresponding to the symbol c, a state transition having an input symbol and an output symbol c and a weight unigram probability (logarithm negative value) -logP ^* (c) is created (Fig. 7). However, only one state corresponding to the symbol ε is prepared in the constructed WFST.

Such a backoff N-gram model WFST can be constructed according to the following procedure.
Step 1: A state S (ε) corresponding to the symbol ε is created, i = S (ε), and added to the state set Q and the end state set F.
Step 2: Repeat step 3 for n = 1,..., N-1.
Step 3: For each registered n-gram probability P (w _n | w ₁ ^n-1 ), steps 3.1 to 3.3
repeat.

Step 3.1: A state S (w ₁ ⁿ ) corresponding to the symbol string w ₁ ⁿ⁻¹ is created and added to the state set Q.
Step 3.2: Create a state transition <S (w ₁ ⁿ⁻¹ ) → S (w ₁ ⁿ ), w _n : w _n / −logP (w _n | w ₁ ⁿ⁻¹ )>.
Step 3.3: Create a state transition <S (w ₁ ⁿ ) → S (w ₂ ⁿ ), ε: ε / −log α (w ₁ ⁿ )> for backoff.
Step 4: For each registered N-gram probability P (w _N | w ₁ ^N−1 ), state transition <S (w ₁ ^N−1 ) → S (w ₂ ^N ), w _N : w _N / − logP (w _N | w ₁ ^N-1 )> is created.
Here, S (x) is a function that maps the symbol string x to the corresponding state number.
If the WFST constructed by this procedure is used, the symbol string can be converted according to the procedure of FIG.

The first embodiment will be described based on the above premise knowledge.
[First embodiment]
FIG. 8 shows a functional block diagram of the symbol string converter 100 according to the first embodiment.

  The symbol string conversion apparatus 100 according to the first embodiment includes a symbol string acquisition unit 103, a hypothesis expansion unit 804, a hypothesis narrowing unit 105, a symbol string output unit 106, a symbol string model storage unit 807, an initial state acquisition unit 808, and state transitions. A set acquisition unit 809 and an end state determination unit 810 are included. The processing contents of the symbol string acquisition unit 103, hypothesis expansion unit 804, hypothesis narrowing unit 105, and symbol string output unit 106 are the same as those in FIG. As described above, in this embodiment, the WFST of the back-off N-gram model representing the symbol string model is not constructed in advance, so that instead of the WFST storage unit 101, the symbol string model storage unit 807, the initial state acquisition unit 808, the state A transition set acquisition unit 809 and an end state determination unit 810 are included. With such a configuration, instead of referring to the WFST, the state transition set acquisition unit 809 is replaced with a process of acquiring a necessary part of the WFST state and state transition from the symbol string model storage unit 807. 4 is different. As a result, the symbol string can be converted without converting all the symbol string models into WFST and storing them in the WFST storage unit 101 in advance.

<Symbol string model storage unit 807>
The symbol string model storage unit 807 stores an L-gram probability and a back-off coefficient α (w ₁ ^{L ′} ) for each symbol string w ₁ ^{L ′ in} association with each other. However, L = 1, 2,..., N and L ′ = 1, 2,. Each symbol string w ₁ ^{L ′} corresponds to an L ′ gram probability. For example, when N = 3, trigram probability P (w _i | w _{i -2} ^i-1 ), bigram probability P (w _i | w _i-1 ), unigram probability P (w _i ), and bigram probability P (w _i │w _i-1) corresponding to the back-off factor ^{_{α (ww i-2 i-}} 1) and the unigram probability P backoff factor corresponding to _{(w i) α (w i} ) and are stored . The L-gram probability is calculated by equation (4) using the appearance probability P (w ₁ ^L ) of the symbol string w ₁ ^L (1 ≦ L ≦ N) consisting of N or less symbols (N is a positive integer). (However, the probability of occurrence is used in place of the number of occurrences in Equation (4)). Further, the back-off coefficient α (w ₁ ^{L ′} ) can be obtained by Expression (6). For example, it is assumed that it is stored in a table format as shown in FIG. FIG. 12 will be described in detail later. In FIG. 12, the probability P (w _i | w _{i-L + 1} ^i-1 ) that the symbol w _i occurs depending on the symbol string w _{i-L + 1} ^i-1 is slightly reduced (corrected). ) The value P ^* (w _i │w _{i-L + 1} ^i-1 ) is stored, but instead of P ^* (w _i │w _{i-L + 1} ^i-1 ), P (w _i │w _{i-L + 1} ^i-1 ) may be stored. Let P (w _i │w _{i-L + 1} ^i-1 ) and P ^* (w _i │w _{i-L + 1} ^i-1 ) be a symbol string w _{i-L + 1} corresponding to the state q of the transition source. ^It is also referred to as a first probability corresponding to the probability of occurrence of the symbol w _i input depending on ⁱ⁻¹ .

<Hypothesis development unit 804>
The hypothesis expansion unit 804 receives the symbol x from the symbol string acquisition unit 103 and updates each temporary set of the current hypothesis set using the state transition set that can be transitioned from the transition source acquired by the state transition set acquisition unit. And the accumulated weight are output to the hypothesis narrowing-down unit 105.

  If a hypothesis developing unit 804 has not yet generated a hypothesis (when it is executed for the first time), it executes the initial state acquisition means 808 to acquire the state number of the initial state of WFST. Then, the state number of the initial state acquired by the initial state acquisition unit 808 and the symbol x received from the symbol string acquisition unit 103 are sent to the state transition set acquisition unit 809, and a set of states that can be transitioned from the initial state by the symbol x is sent. get. Then, a new hypothesis set is generated using the received state transition set.

  When a hypothesis corresponding to a symbol input so far has already been generated, the hypothesis expansion unit 804 corresponds to a state corresponding to the symbol x received from the symbol string acquisition unit 103 and the state where the current hypothesis has reached. The number q is sent to the state transition set acquisition unit 809, and the state q that the current hypothesis has reached from the state transition set acquisition unit 809 is changed to a state that can be transitioned by the new symbol x received from the symbol string acquisition unit 103. Get a set. Then, a new hypothesis set is generated by updating the state transition process of the current hypothesis using the set of transitionable states acquired from the state transition set acquisition unit 809.

  The hypothesis developing unit 804 calculates a cumulative weight for each hypothesis in the generated hypothesis set. Then, the generated set of hypotheses is sent to the end state determination means 810, and it is determined whether or not each hypothesis has reached the end state. The hypothesis development unit 804 receives the determination result from the end state determination unit 810, and updates the hypothesis accumulated weight by adding the end weight of the end state to the accumulated weight of the hypothesis when it is in the end state. . Then, the generated hypothesis set and its accumulated weight are sent to the hypothesis narrowing unit 105.

<Initial state acquisition means 808>
The initial state acquisition unit 808 generates an initial state of WFST in response to the request of the hypothesis developing unit 804 and outputs the initial state to the hypothesis developing unit 804. This is processing corresponding to the initial state request s [h] = i in S103 of FIG. 4, and returns one state number (for example, i = 0). Then, the generated state number s [h] of the initial state is sent to the hypothesis developing unit 804. Also, a function q = S (w) that uniquely maps the symbol string w to the state number q and its inverse function w = S ⁻¹ (q) are prepared, and S (ε) = 0 and S ⁻¹ (0) = ε. The functions S and S ⁻¹ may be given in advance from the outside. An example of the function will be described later.

<State Transition Set Acquisition Unit 809>
The state transition set acquisition unit 809 receives the input state number q and the input symbol x, and uses these values to generate a state transition set E that can transition from the state number q to the input symbol x according to the procedure of FIG. Ask.

First, in step S901, the state number q and the input symbol x are received in step S902. In step S903, the symbol string w ₁ ^k corresponding to the state q is ^obtained as w ₁ ^k = S ⁻¹ (q). Here, k = 0, 1,..., N−1.

In step S904, an initial value of a variable α (cumulative backoff coefficient) for accumulating the backoff coefficient is set. For example, α = 1. If k <N−1, j = 1, otherwise (k ≧ N−1), j = 2. This j = 2 is set so that when calculating the probability P ^* (x | w _j ^k ), the symbol with the smallest order is truncated so that the length of the symbol string w _j ^k is not greater than N−1. Probability P ^* (x | w _j ^k ) is guaranteed to be L-gram probability (1 ≦ L ≦ N).

In step S905, the symbol string model storage unit 1301, a symbol string w _j ^k dependent to occurring the symbol string x N-gram probability P probability value corresponding to ^{_{(x│w j k) P * (}} x│w j ^k ) If there is (discounted N-gram probability), the process proceeds to step S907; otherwise, the process proceeds to S906. Note that P ^* (x | w _j ^k ) is a symbol stored in the symbol string model storage unit 807 according to the above equation (4) (however, the appearance probability is used instead of the number of occurrences in equation (4)). It can be calculated based on the appearance probabilities of the sequence w ₁ ^k x and the symbol sequence w ₁ ^k . Even if the probability value P ^* (x | w _j ^k ) exists, if the value is smaller than the predetermined threshold value, the process may proceed to S906. Write This was when the probability value is particularly small (relative to 10,000 ^three different symbol strings, if such frequency is 1 or 2) for the probability values may not be trusted, forcibly backoff This is because the accuracy may improve.

Since the step S906 probability P ^* (x│w _j ^k) is the back-off in the absence, back-off factor alpha (correction coefficient) α (w _j ^k) a value obtained by multiplying the new cumulative back-off factor Let α be α (α ← α × α (w _j ^k )). Further, j is incremented by 1 (j ← j + 1) in order to truncate the youngest symbol in the symbol string w _j ^k (to back off). For example, if the discounted trigram probability P ^* (c | ab) of the symbol string abc does not exist, (α ← α × α (ab)) and the lowest order of the symbol string abc (the first The symbol a is truncated to obtain a symbol string bc, and the determination process in S905 is repeated. The back-off coefficient α (w _j ^k ) can be acquired from the symbol string model storage unit 807.

In step S907, since there is a probability P ^* (x│w _j ^k), the state transition ^{_{<q → S (w j k}} x), x: x / -log (αP * (x│w j k))> And a state transition list (a set of state transitions) E having this as the only state transition is generated. Note that a value −log (αP ^* (x | w _j ^k ) corresponding to the product of the probability P ^* (x | w _j ^k ) and the cumulative back-off coefficient α is also referred to as a second probability. It is set as the state transition probability of the state transition that transitions from the state reached by each hypothesis of the set of hypotheses to the input symbol x.

  Finally, in step S908, the acquired state transition list E is output to the hypothesis developing unit 804, and the processing of the state transition set acquiring unit 809 is terminated.

  According to the procedure of FIG. 9, when the state transition of FIG. 5 is made by the conventional method, the same state transition is also made in this embodiment. When the state transition of FIG. 6 is made by the conventional method, the state transition as shown in FIG. 10 is made (however, the broken line portion is written for comparison with FIG. 6 and is created in this embodiment. Not) When the state transition of FIG. 7 is made by the conventional method, the state transition as shown in FIG. 11 is made (however, the broken line portion is written for comparison with FIG. 7 and is created in this embodiment. Not) Although the state transitions of the states ab, b, and bc in FIG. 6 are combined into one in FIG. 10, the input symbols, the output symbols, and the cumulative weights are all the same. Similarly in FIG. 7, the state transitions of the state transitions ab, b, ε, and c are combined into one transition in FIG. 11, but the input symbol, the output symbol, and the cumulative weight match.

<End state determination means 810>
The end state determination means 810 receives the hypothesis list (hypothesis set) H generated by the hypothesis expansion unit 804, and whether or not the state s [h] in which each hypothesis h in the hypothesis set H has reached is the end state. That is, it is determined whether or not s [h] ∈F. For example, if state S (ε) = 0 is the only end state, it may be determined that if s [h] = 0, it is the end state, and if s [h] ≠ 0, it is not the end state. This corresponds to the process of S113 in FIG. The end state determination unit 810 outputs the determination result to the hypothesis development unit 804.

  The symbol string model storage unit 807, the initial state acquisition unit 808, the state transition set acquisition unit 809, and the end state determination unit 810 do not create a WFST from the symbol string model in advance, but each time an input symbol is read, Only a conversion rule (that is, only partial WFST) that can be converted by an input symbol is generated from the current state with reference to the symbol string model, and symbol string conversion can be performed using this. Thereby, even if the contents (probability values and correction coefficient values) stored in the symbol string model are updated during the symbol string conversion, the symbol string conversion can be performed without reconfiguring the WFST. It is possible to dynamically generate WFST and perform symbol string conversion faster than before.

<Hypothesis narrowing unit 105>
The hypothesis narrowing-down unit 105 receives the set of hypotheses generated by the hypothesis expansion unit 804, and deletes hypotheses other than a predetermined number of hypotheses from hypotheses having the minimum or maximum cumulative weight among hypotheses reaching the same state. To narrow down the hypothesis. If the input symbol string has been read to the end, the hypothesis h having the smallest or largest cumulative weight (W [h]) is selected from the hypotheses that have reached the end state, and the output symbol string O [h] is sent to the symbol string output unit 106 as a symbol string conversion result. If the input symbol string has not been read to the end, the set of refined hypotheses is sent to the hypothesis developing unit 804.

<Symbol string output unit 106>
The symbol string output unit 106 outputs the output symbol string received from the hypothesis narrowing unit 105 as a symbol string conversion result.

<Example of function that maps symbol string to state number>
Next, a method for efficiently obtaining the state S (w ₁ ^k ) corresponding to the symbol string w ₁ ^k will be described. For example, assume that the individual probabilities of the symbol string model are arranged in the memory of a computer as shown in FIG. 12 (in the case of a back-off trigram). Here, M symbols v _m (m = 1 to M) are registered in the table of unigram probabilities, and each of its entries has symbol id number m, unigram probability P (v _m ), back A pointer next pointing to the head of the entry storing the off coefficient α (v _m ) and the subsequent bigram is included. In the example of FIG. 12, each pointer of a unigram points to the head of an entry storing a bigram that follows with an arrow. Similarly, the bigram table indicates the head of the entry in which the symbol id number n, bigram probability P (v _n | v _m ), backoff coefficient α (v _m v _n ), and the following trigram are stored. Pointer next is included. However, if there is no symbol that follows, substitute null for next. This corresponds to this case in FIG. Further, the trigram table includes the symbol id number r and the trigram probability P (v _r | v _m v _n ). Therefore, if a permutation of id numbers m, n, r of any three symbols is given, the next of the m + 1th unigram entry from the entry of the bigram table pointed to by next from the mth entry of the unigram The bigram entry matching the id number n is searched before the bigram table entry pointed to by. If a bigram entry matching the id number n is found, the trigram matching the id number r from the trigram table entry pointed to by the next to the entry in the trigram table pointed to by the next n + 1 bigram entry next Look for an entry. If a matching id number is not found in the process of sequentially tracing from the unigram, the permutation (m, n, r) consisting of the three id numbers m, n, r is changed to the permutation (n, r) consisting of the two id numbers. The entry of the bigram table entry pointed to by next of the next unigram entry of the unigram entry matching the id number n from the entry of the bigram table pointed to by the next unigram entry matching the id number n from the unigram table entry Find the bigram entry matching the id number r until the foreground. If found, the value obtained by multiplying the probability by the cumulative back-off factor is returned as the trigram probability. If not found, a permutation (n, r) consisting of two id numbers is changed to a permutation (r) consisting of one id number, and a unigram entry matching the id number r is searched from the unigram entry table, and its probability Is multiplied by the cumulative backoff factor and returned as the trigram probability.

For example, given three permutations of id numbers 1,2,4 (1,2,4), follow the link from 1 to the next in the unigram entry until 2 in the bigram entry, but follow The trigram entry is 1,9,13, so 4 is not found. Therefore, for backoff, first, the backoff coefficient α (v ₁ v ₂ ) of the reached bigram entry v ₁ v ₂ is stored. Next, the first id number 1 is deleted, and the second link of the unigram entry 2 is followed using the permutation (2, 4) of the two id numbers 2, 4 to find the bigram entry 4. Here, the back-off trigram probability is calculated as α (v ₁ v ₂ ) P (v ₄ | v ₂ ). If the permutation (2, 4) of the two id numbers 2 and 4 is not found after back-off, the id number 2 is further deleted to back off to the unigram with the id number 4. Here, the back-off trigram probability is calculated as α (v ₁ v ₂ ) α (v ₂ ) P (v ₄ ).

Using this data structure, for example, the state S (w ₁ ^k ) matches up to k gram by tracing the entry with the matching id number from the unigram table using the id number sequence corresponding to w ₁ ^k. When the id number is found, the relative position on the memory where the found entry is stored is assigned as the state number starting from the position (address) on the memory where the first entry of the unigram is stored. Can do. However, it is assumed that L-gram entries (L-gram probabilities, backoff coefficient α (w ₁ ^{L ′} ) for each symbol string w ₁ ^{L ′,} etc.) are continuously arranged on the memory. That is, information on all the symbol strings included in the symbol string model storage unit 807 and their chain probabilities are stored in succession on the computer memory. By doing so, a value proportional to the relative address number obtained by subtracting an arbitrary starting address number from the memory address number of each symbol string is set as the state number. If state number 0 is assigned to symbol ε,
Status number = (address of found entry-address of first entry in unigram)
÷ (memory size for one entry up to N-1 grams) + 1
... (1)
Can be obtained as This is an example of function S. Conversely, the entry corresponding to the state number is the entry address = (state number-1).
× (Memory size for one entry up to N-1 grams)
+ Address of the first entry in the unigram
... (2)
Is required. As a result, the symbol string S ⁻¹ (q) corresponding to the arbitrary state number q can also be efficiently obtained.

<Modification>
The symbol string model storage unit 807 may store the appearance probability (occurrence probability) of the symbol string instead of the L-gram probability. Each unit that requires the L-gram probability may calculate the L-gram probability using the expression string (4) using the appearance probability of the symbol string.

[Second Embodiment]
In the second embodiment, an example in which the present invention is applied to speech recognition will be described.
The symbol string model is a back-off trigram language model and is stored in the memory with a data structure as shown in FIG.

  FIG. 13 is a functional block diagram of the speech recognition apparatus 200 of the second embodiment. A language comprising a backoff trigram language model storage unit 1307, an initial state acquisition unit 1308, a state transition set acquisition unit 1309, and an end state determination unit 1310 instead of the language model WFST storage unit of the conventional method (see Patent Document 1). The difference is that a model WFST generation unit 130 is provided.

  That is, using the back-off trigram language model storage unit 1307, the initial state acquisition unit 1308, the state transition set acquisition unit 1309, and the end state determination unit 1310, a state transition set of the language model WFST is generated as necessary. The symbol string conversion is performed as if the language model WFST exists.

  Note that the processing of each unit other than the back-off trigram language model storage unit 1307, the initial state acquisition unit 1308, the state transition set acquisition unit 1309, and the end state determination unit 1310 is described in detail in Patent Document 1. Here, only the outline will be described.

<Audio signal input unit 1303 and audio feature symbol string extraction unit 1304>
The voice signal sent from the voice signal input unit 1303 for inputting voice is converted into an acoustic feature symbol string by the voice feature symbol string extraction unit 1304 that extracts a time series of the short-time acoustic pattern of the voice as a symbol string, and the sound The feature symbol string is input to the symbol string conversion unit 1305 that performs symbol string conversion.

<Symbol string conversion unit 1305>
The symbol string conversion unit 1305 includes a hypothesis expansion unit 1306, a hypothesis correction unit 1311, and a hypothesis narrowing unit 1312.

  The symbol string conversion unit 1305 retains the characteristics of a standard acoustic pattern sequence of fixed speech units (for example, phonemes) from the acoustic model storage unit 1301 and gives a similarity between each fixed speech unit and an arbitrary acoustic pattern. Read the acoustic model.

The acoustic pattern used for speech recognition includes mel-frequency cepstral coefficients (mel-frequency cepstral coefficients, obtained by analyzing the speech signal every short time (for example, 10 milliseconds).
(Referred to as MFCC), delta MFCC, LPC cepstrum, log power, etc.

  As an acoustic model that retains standard features of various speech fixed units (for example, phonemes), for example, a hidden Markov model method (Hidden Markov Model, which models a set of sequences of acoustic patterns based on probability / statistical theory) (Hereinafter referred to as HMM) is the mainstream. Details of the HMM method are disclosed in, for example, “Recognition of Speech by Stochastic Model” by Seichi Nakagawa, Institute of Electronics, Information and Communication Engineers. Other conventional techniques may be used as the acoustic model.

  Further, the hypothesis developing unit 1306 reads from the word dictionary WFST storage unit 1302 a word dictionary WFST for converting the sequence of fixed speech units into a sequence of words having the pronunciation. Further, the symbol string conversion unit 1305 uses the language model WFST generation unit 130 instead of the language model WFST storage unit to generate a state transition set of the language model WFST as necessary, and an acoustic feature symbol string extraction unit 1304. Is read out, an output symbol string having the smallest cumulative weight is obtained, and is sent to the symbol string output unit 131.

(Hypothesis expansion unit 1306)
The hypothesis developing unit 1306 reads the symbols of the acoustic feature symbol string sent from the acoustic feature symbol string extracting unit 1304 one by one. Next, the hypothesis developing unit 1306 adds a new state transition to each of the current hypothesis set using the word dictionary WFST.

  Note that the score of the acoustic feature symbol (acoustic pattern) calculated by the acoustic model is used as the weight of the word dictionary WFST. However, the larger the score, the closer the input acoustic pattern is to the sound fixed unit represented by the acoustic model, so a negative acoustic score is used as the weight. In the calculation of the acoustic score by the hidden Markov model, for example, a probability value based on a Gaussian distribution is used.

(Hypothesis correction unit 1311)
The hypothesis correction unit 1311 receives a set of hypotheses to which new state transitions are added. The hypothesis correction unit 1311 uses the initial state acquisition unit 1308, the state transition set acquisition unit 1309, and the end state acquisition unit 1310 to refer to the backoff trigram language model stored in the backoff trigram language model storage unit 1307. The accumulated weights of the individual hypotheses received from 1306 are corrected.

  Specifically, a symbol string output from the state transition process of each hypothesis received from the hypothesis expansion unit 1306 is used as an input symbol string and corresponds to an output symbol string output by performing the same processing as in the first embodiment. The cumulative weight of each hypothesis is updated by adding the cumulative weight of the hypothesis, that is, the cumulative weight of the state transition process having the smallest cumulative weight among the possible state transition processes, to the cumulative weight of the hypothesis. In other words, the hypothesis correction unit 1311 uses a word dictionary WFST that is partially generated by the language model WFST generation unit 130 and that is different from the WFST corresponding to the back-off trigram language model, and uses the acoustic feature symbol string (back Backoff the symbol string output from the state transition process of the hypothesis included in the set of hypotheses obtained by converting the symbol string different from the input symbol string for WFST corresponding to the off-trigram language model The same processing as in the first embodiment is performed as an input symbol string for WFST corresponding to the trigram language model.

(Hypothesis narrowing part 1312)
The hypothesis narrowing-down unit 1312 receives the set of hypotheses generated by the hypothesis correction unit 1311 and deletes hypotheses other than a predetermined number of hypotheses from hypotheses having the minimum or maximum cumulative weight among hypotheses reaching the same state. To narrow down the hypothesis. If the input symbol string has been read to the end, the hypothesis having the smallest or maximum cumulative weight is selected from the hypotheses that have reached the end state, and the output symbol string is used as the symbol string conversion result, The data is sent to the column output unit 1313. If the input symbol string has not been read to the end, a predetermined number of hypotheses with large accumulated weights are deleted and narrowed down, and then the remaining hypothesis set is sent to the hypothesis developing unit 1306.

The hypothesis developing unit 1306 reads the next symbol of the acoustic feature symbol string, and the symbol string converting unit 1305 repeats the same processing until all the input voice feature symbol strings are read.
After reading the last speech feature symbol, the hypothesis narrowing-down unit 1312 obtains the hypothesis with the minimum cumulative weight and its output symbol string, and sends it to the symbol string output unit 1313.

<Symbol string output unit 1313>
The symbol string output unit 1313 outputs the received output symbol string as a speech recognition result.
With such a configuration, the present invention can be used for speech recognition.

[Modification]
When calculating the probability value P (x | w _j ^k ) in the state transition set acquisition unit 809 of the first embodiment, a unigram probability distribution P _T (x) depending on the current topic is introduced, and this probability distribution is introduced. For example, based on
P ^ '(x│w _j ^k ) = λP _T (x) + (1-λ) P (x│w _j ^k ) (7)
As described above, the probability value may be dynamically updated according to the current topic. For example, the unigram probability distribution P _T (x) may be estimated from the frequency of recently spoken words. Here, λ is a coefficient that balances the unigram probability that depends on the topic and the N-gram probability that does not depend on the topic, and 0 ≦ λ ≦ 1.

  Alternatively, a symbol string conversion unit includes a probability value update unit that updates the appearance probability of the symbol string stored in the symbol string model storage unit 807 of the first embodiment and the back-off trigram language model storage unit 1307 of the second embodiment. In the middle of speech recognition processing, the appearance probabilities of some symbol strings stored in the symbol string model or the back-off trigram language model storage unit may be updated.

[Experimental example]
A speech recognition system was constructed in the form shown in FIG. The acoustic model has HMMs for 43 types of phonemes, each phoneme has 3 states, and each state has its phoneme context (what is the phoneme in front and what is the phoneme that follows) Accordingly, one of the probability density distributions of 3011 types of acoustic patterns is assigned. The Id numbers of these probability density distributions were used as voice fixed units.

  The audio signal acoustic pattern series is obtained by analyzing the audio signal every 10 milliseconds, and delta MFCC12 which is a linear regression coefficient when two frames before and after are viewed in the time series direction of each dimension of MFCC. It is extracted as an input sequence whose elements are dimensional and delta MFCC 12 dimensions, which are primary regression coefficients when two frames are viewed in the time series direction of each dimension, and the dimensional delta MFCC 12 dimensions and logarithmic power.

  Using 100,000 words and their pronunciation as a dictionary, we built a WFST that converts a fixed speech unit sequence to a word sequence.

  In the back-off trigram language model, the back-off trigram probability was estimated from the word string created from the 2762 lecture of “Japanese Spoken Language Corpus” and stored in a data structure as shown in FIG.

Furthermore, when recognizing lecture speech, the topic-dependent unigram in equation (7) is introduced, and this unigram probability is estimated from the already obtained speech recognition results while recognizing the lecture speech. I also evaluated the method used to recognize future speech. This is a method called a cache language model, and P _T (w _i ) in equation (7) is calculated as follows.
P _T (w _i ) = P _cache (w _i │w ₁ ^(i-1) ) = (C _K (w _i )) / K (8)

Here, K is a natural number indicating how many past words among the already recognized words are considered, and C _K (w _i ) represents the number of occurrences of w _{i in} the recently recognized K words. This cache language model is a model based on an empirical rule that “a word spoken once is easy to be spoken again” when a person speaks on a certain theme. The cache language model is disclosed in Non-Patent Documents 1 and 2 and Reference Document 1.
(Reference 1) R. Kuhn and R. De Mori, “A cache-based natural language model for speech recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no.6, pp. 570-583 , 1990

  Table 1 shows the recognition processing time and the recognition error rate of the conventional method and this embodiment. In the case of this embodiment, the case where a cache language model is used is also shown. The real time ratio is a value obtained by dividing the recognition processing time by the actually spoken time, and the smaller the value, the higher the speed. The recognition processing time was obtained by measuring the time when speech recognition was performed using Intel Xeon (registered trademark) X5570 2.93 GHz processor and 10 lectures (12 minutes each) were recognized. The word error rate represents the proportion of words that are mistakenly recognized among words that are actually spoken. The smaller the word error rate, the higher the accuracy of speech recognition.

  In the results shown in Table 1, in the conventional method, the speech recognition is performed by converting the back-off trigram language model into WFST in advance. In this embodiment, a state transition is generated from a back-off trigram language model as necessary, and voice recognition is performed. When these are compared, it can be seen that the word error rate is the same and the real time ratio representing the processing time is substantially the same. However, in the conventional method, it took about 8 seconds using an Intel Xeon (registered trademark) X5570 2.93 GHz processor to convert the back-off trigram language model to WFST.

When a cache language model is used, if one utterance (about 5 to 10 seconds) is recognized and P _T (w _i ) is updated every time WFST is recreated, the processing amount of the entire speech recognition greatly increases. Assuming 100 utterances per lecture, if the processing time of 800 seconds is added and the actual utterance time is 12 minutes = 720 seconds, the real time ratio is expected to be 1.2-1.3.

  In the case of the present embodiment shown in Table 1 (with a cache language model), the cache language model can be used without recreating the WFST, the processing time does not increase, and the real-time ratio is 0.31. The word error rate was reduced to 18.4% due to the effect of the cache language model.

<Other variations>
The present invention is not limited to the above-described embodiments and modifications. For example, the various processes described above are not only executed in time series according to the description, but may also be executed in parallel or individually as required by the processing capability of the apparatus that executes the processes. In addition, it can change suitably in the range which does not deviate from the meaning of this invention.

<Program and recording medium>
In addition, various processing functions in each device described in the above embodiments and modifications may be realized by a computer. In that case, the processing contents of the functions that each device should have are described by a program. Then, by executing this program on a computer, various processing functions in each of the above devices are realized on the computer.

  The program describing the processing contents can be recorded on a computer-readable recording medium. As the computer-readable recording medium, for example, any recording medium such as a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory may be used.

  The program is distributed by selling, transferring, or lending a portable recording medium such as a DVD or CD-ROM in which the program is recorded. Further, the program may be distributed by storing the program in a storage device of the server computer and transferring the program from the server computer to another computer via a network.

  A computer that executes such a program first stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its storage unit. When executing the process, this computer reads the program stored in its own storage unit and executes the process according to the read program. As another embodiment of this program, a computer may read a program directly from a portable recording medium and execute processing according to the program. Further, each time a program is transferred from the server computer to the computer, processing according to the received program may be executed sequentially. Also, the program is not transferred from the server computer to the computer, and the above-described processing is executed by a so-called ASP (Application Service Provider) type service that realizes the processing function only by the execution instruction and result acquisition. It is good. Note that the program includes information provided for processing by the electronic computer and equivalent to the program (data that is not a direct command to the computer but has a property that defines the processing of the computer).

  In addition, although each device is configured by executing a predetermined program on a computer, at least a part of these processing contents may be realized by hardware.

Claims (8)

A symbol string conversion method for converting an input symbol string to an output symbol string,
A symbol string acquisition step of acquiring symbols one by one in order from the top of the input symbol string;
The occurrence probability of a symbol string consisting of one or more symbols, using as input the symbol acquired in the symbol string acquisition step and state information that is information specifying the state in which each hypothesis of the current set of hypotheses has reached With reference to the symbol string model storage unit in which the chain probability is stored as a symbol string model, the state that the current hypothesis has reached is a transition source, and the state transition that can be transitioned from the transition source and the state transition probability A state transition set acquisition step for acquiring
A hypothesis expansion step of updating each hypothesis in the current hypothesis set using a set of state transitions that can be transitioned from the transition source acquired in the state transition set acquisition step;
Narrowing down hypotheses by deleting hypotheses other than a predetermined number of hypotheses from the hypothesis set that has reached the same state among the hypothesis sets updated in the hypothesis expansion step, If the input symbol string has been read to the end in the symbol string acquisition step, a hypothesis narrowing step for obtaining, as the output symbol string, a hypothesis having the maximum or minimum cumulative weight among hypotheses that have reached an end state; ,
Symbol string conversion method including The symbol string conversion method according to claim 1,
The chain probability is a probability that a certain symbol occurs and a probability that a certain symbol occurs depending on a symbol string composed of one or more symbols.
In the symbol string model storage unit, in addition to the chain probability, a back-off coefficient of a symbol string corresponding to each chain probability is stored in association with each other,
The state transition set acquisition step includes:
An initial value setting step for setting an initial value of the cumulative backoff coefficient;
When the first probability corresponding to the probability that the input symbol occurs depending on the symbol string corresponding to the state of the transition source exists in the symbol string model storage unit, the first probability is acquired. 1 probability acquisition step;
When the first probability does not exist or is smaller than a predetermined threshold, a back-off coefficient of a symbol string corresponding to the state of the transition source is acquired from the symbol string storage unit, and the acquired back-off coefficient is A cumulative backoff coefficient update step of updating the cumulative backoff coefficient by multiplying by a cumulative backoff coefficient;
When the first probability exists or is equal to or greater than a predetermined threshold, a second probability that is a value corresponding to a product of the first probability and the cumulative back-off coefficient is set to the current hypothesis set. A state transition probability setting step for setting as a state transition probability of a state transition that transitions from the state reached by each hypothesis to the input symbol;
Symbol string conversion method including The symbol string conversion method according to claim 1 or 2,
Information of all the symbol strings included in the symbol string model storage unit and the chain probability thereof are continuously stored on the memory of the computer,
The state information is a value proportional to a relative address number obtained by subtracting an arbitrary starting address number from the memory address number of each symbol string,
Symbol string conversion method. The symbol string conversion method according to any one of claims 1 to 3,
Included in a set of hypotheses obtained by converting a second input symbol string that is an input symbol string different from the input symbol string using one or more WFSTs different from the WFST corresponding to the symbol string model A symbol string output from any hypothetical state transition process is an input symbol string for the symbol string model,
Symbol string conversion method. A speech recognition method for converting an acoustic feature symbol string obtained from speech into speech recognition results,
A symbol string acquisition step of acquiring symbols one by one in order from the top of the acoustic feature symbol string;
A hypothesis expansion step of adding a new state transition to each of the current set of hypotheses with reference to the word dictionary WFST using the score of the acoustic feature symbol calculated by the acoustic model as a weight;
Symbol information output from each state transition process of the set of hypotheses to which state transition has been added in the hypothesis expansion step, and state information that is information identifying the state reached by each hypothesis in the current set of hypotheses And the transition of the state where the current hypothesis has reached by referring to the language model storage unit in which the occurrence probability or the chain probability of a symbol string composed of one or more symbols is stored as a symbol string model As a source, a state transition set acquisition step for acquiring a state transition that can be transitioned from the transition source and its state transition probability;
A hypothesis correction step of updating each hypothesis in the set of current hypotheses using a set of state transitions that can be transitioned from the transition source acquired in the state transition set acquisition step;
Narrowing down hypotheses by deleting hypotheses other than a predetermined number of hypotheses from the hypothesis set that has reached the same state among the hypothesis sets updated in the hypothesis expansion step, If the input symbol string has been read to the end in the symbol string acquisition step, a hypothesis narrowing step for obtaining, as the speech recognition result, a hypothesis having the maximum or minimum cumulative weight among hypotheses that have reached an end state; ,
A speech recognition method including: A symbol string converter for converting an input symbol string into an output symbol string,
A symbol string acquisition unit for acquiring symbols one by one in order from the top of the input symbol string;
The occurrence probability of a symbol string consisting of one or more symbols, using as input the symbol acquired by the symbol string acquisition unit and state information that is information identifying the state in which each hypothesis of the current hypothesis set has reached With reference to the symbol string model storage unit in which the chain probability is stored as a symbol string model, the state that the current hypothesis has reached is a transition source, and the state transition that can be transitioned from the transition source and the state transition probability A state transition set acquisition unit for acquiring
A hypothesis expansion unit that updates each hypothesis in the current set of hypotheses using a set of state transitions that can be transitioned from the transition source acquired by the state transition set acquisition unit;
Narrowing down hypotheses by deleting hypotheses other than a predetermined number of hypotheses from the hypothesis that has reached the same state among the hypothesis set updated in the hypothesis expansion unit, If the input symbol string is read to the end in the symbol string acquisition unit, a hypothesis narrowing-down unit that determines a hypothesis having the maximum or minimum cumulative weight as the output symbol string from among the hypotheses that have reached the end state; ,
A symbol string conversion device including: A speech recognition device that converts an acoustic feature symbol string obtained from speech into speech recognition results,
A symbol string acquisition unit for acquiring symbols one by one in order from the top of the acoustic feature symbol string;
A hypothesis expander that adds a new state transition to each of the current set of hypotheses, with reference to the word dictionary WFST using the score of the acoustic feature symbol calculated by the acoustic model as a weight,
State information that is a symbol string output from each state transition process of the set of hypotheses to which state transition is added in the hypothesis expansion unit and information for specifying the state reached by each hypothesis in the current set of hypotheses And the transition of the state where the current hypothesis has reached by referring to the language model storage unit in which the occurrence probability or the chain probability of a symbol string composed of one or more symbols is stored as a symbol string model As a source, a state transition set acquisition unit for acquiring a state transition that can be transitioned from the transition source and its state transition probability,
A hypothesis correction unit that updates each hypothesis of the current hypothesis set using a set of state transitions that can be transitioned from the transition source acquired by the state transition set acquisition unit;
Narrowing down hypotheses by deleting hypotheses other than a predetermined number of hypotheses from the hypothesis that has reached the same state among the hypothesis set updated in the hypothesis expansion unit, If the input symbol string has been read to the end in the symbol string acquisition unit, a hypothesis narrowing-down unit that obtains a hypothesis with the maximum or minimum cumulative weight among the hypotheses reaching the end state as the speech recognition result; ,
A speech recognition device.   The program for making a computer perform each step of the symbol sequence conversion method in any one of Claims 1 thru | or 4.

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