JP2012063651A - Voice recognition device, voice recognition method and voice recognition program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a voice recognition technique that can achieve highly robust voice recognition while restraining increases in calculation cost and memory capacity required for calculation.SOLUTION: A voice recognition device stores a plurality of sound models having the same structure and a search network common to the plurality of sound models; accepts a voice input; extracts a sound feature quantity by using the inputted voice; calculates a score, correspondingly to each of the plurality of sound models, for each route representing a series of nodes passed on the way from a start point node to an end point node on the search network by using the extracted sound feature quantity, the plurality of sound models and the search network; by selecting a first route whose score is the greatest among the routes, retrieves the first route which is the optimum route from the start point node to the end point node on the search network; and outputs information indicating the first route, which is the result of voice recognition.

Description

  Embodiments described herein relate generally to a speech recognition apparatus, a speech recognition method, and a speech recognition program.

  Conventionally, in order to perform highly robust speech recognition in a speech recognition device, for example, as disclosed in Patent Document 1 and Patent Document 2, a plurality of path search units ( A method of calculating a path composed of a plurality of optimum word sequences by using the decoder simultaneously, or a plurality of optimum paths by sequentially using a single path search unit as disclosed in Patent Document 3. There was a way to calculate a path consisting of a sequence of words.

JP 2005-221678 A JP 2003-108188 A JP 2007-225931 A

  However, in the conventional method, there is a risk that the calculation cost for performing highly robust speech recognition and the amount of memory used for the calculation increase. In particular, in a method of performing calculation using a plurality of acoustic models, there is a risk that the calculation cost and the amount of memory used for the calculation increase significantly.

  The speech recognition apparatus according to the embodiment includes a first storage unit that stores a plurality of acoustic models having the same structure, a start node that represents a start point, a terminal node that represents a terminal end, and at least between the start node and the terminal node. A second storage unit that stores a search network common to a plurality of the acoustic models, a reception unit that receives voice input, and an extraction unit that extracts an acoustic feature using the voice And using the acoustic feature quantity, the plurality of acoustic models, and the search network, for each path indicating a sequence of nodes that pass from the start node to the end node on the search network. A calculation unit that calculates a score corresponding to each of the plurality of acoustic models, and the score corresponding to at least one acoustic model of each of the paths is a maximum. A search unit that searches for the first route that is the optimal route from the start node to the end node on the search network, and the first recognition result is the voice recognition result. And an output unit that outputs information indicating a route.

The figure which illustrates the functional structure of the speech recognition apparatus which concerns on this Embodiment. The figure which illustrates a search network. The figure for demonstrating the search of the optimal path | route by the method of token passing. The flowchart which shows the procedure of a speech recognition process. The flowchart which shows the detailed procedure of the process of step S3 of FIG. The figure which shows the example of an update of a cumulative score list. The figure which shows the example of an update of a cumulative score list. The figure for demonstrating the search of the optimal path | route which concerns on this modification.

[First embodiment]
First, the hardware configuration of the speech recognition apparatus will be described. The speech recognition apparatus according to this embodiment includes a control unit such as a CPU (Central Processing Unit) that controls the entire apparatus, a ROM (Read Only Memory) that stores various data and various programs, a RAM (Random Access Memory), and the like. Main storage unit, an auxiliary storage unit such as an HDD (Hard Disk Drive) or CD (Compact Disk) drive device for storing various data and various programs, and a bus for connecting these, a normal computer The hardware configuration is used. In addition, a voice input unit to which voice is input is connected to the voice recognition device by wire or wireless.

  Next, the functional configuration of the speech recognition apparatus according to the present embodiment in such a hardware configuration will be described with reference to FIG. The speech recognition apparatus 100 includes a speech input reception unit 101, an acoustic feature amount extraction unit 102, an acoustic model storage unit 103, a search network storage unit 104, and a route search unit 105. The voice input reception unit 101, the acoustic feature amount extraction unit 102, and the route search unit 105 are each realized by the control unit executing various programs stored in the main storage unit and the auxiliary storage unit. The acoustic model storage unit 103 and the search network storage unit 104 are configured as an auxiliary storage unit, for example.

  The voice input reception unit 101 receives an input of an analog voice signal representing the voice input to the voice input unit and converts it into a digital voice signal. The acoustic feature quantity extraction unit 102 extracts an acoustic feature quantity for each time using the voice signal converted into digital by the voice input reception unit 101. The acoustic model storage unit 103 stores a plurality (for example, M) of acoustic models. As the acoustic model, for example, a hidden Markov model (HMM) in units of phonemes may be used. However, the plurality of acoustic models have a common structure that can be searched by the route search unit 105 using a single search network stored in the search network storage unit 104 described later. For example, when the acoustic model is a hidden Markov model in units of phonemes, the number of states of corresponding phoneme models and the structure of state transitions are common among a plurality of acoustic models.

  The search network storage unit 104 stores a single search network. FIG. 2 is a diagram illustrating a search network. The search network is composed of nodes represented by circles in the drawing and arcs representing transitions between the nodes by arrows. Among the nodes, the node representing the starting point is the starting node S, and the node representing the terminal is the terminal node E. A path indicating a sequence of nodes from the start node S to the end node E is a path. A label is given to some or all of each node and each arc. This label may be an arbitrary identifier for identifying each node and arc on the search network, or may be a phoneme or word associated with each node or arc. It can be changed depending on the result. Also, it is not necessary to label all nodes and arcs. In the example of the search network shown in the figure, each node is given a numerical identifier, and each arc is given a word. Specifically, the labels “1” to “6” are assigned to the nodes, and the label “word 1” is assigned to the arc from the node 2 to the node 3. A label “word 2” is assigned to the arc to reach, and a label “word 3” is assigned to the arc from node 1 to node 4.

The node n of the search network has an acoustic score list that holds the acoustic scores l _m ^{(t, n)} corresponding to the m-th (1 ≦ m ≦ M) acoustic model at time t by the number of acoustic models (M). It has scr (t, n). scr (t, n) is expressed by, for example, Equation 1.

  However, the acoustic score list only needs to be able to refer to the one corresponding to each node, and is not limited to being stored in the node itself.

On the other hand, the arc from node j to node i has a transition score list trans () that holds transition scores a _m ^ji corresponding to the m-th (1 ≦ m ≦ M) acoustic model by the number (M) of acoustic models. j, i). trans (j, i) is represented by, for example, Formula 2.

  However, the transition score list need only be able to refer to the one corresponding to each arc, and is not limited to being held in the arc itself.

  The route search unit 105 uses the acoustic feature amount extracted by the acoustic feature amount extraction unit 102, the plurality of acoustic models stored in the acoustic model storage unit 103, and the search network stored in the search network storage unit 104. The optimum route (referred to as the optimum route) is searched as a result of the recognition of the speech input to the speech input unit. At this time, the path search unit 105 sequentially searches for the optimum path from the start node S to the node for each node that passes from the start node S to the end node E, thereby starting from the start node S to the end node E. The optimal route to reach is searched. The route search unit 105 then displays a route history indicating at least one of a label attached to a node passing through an optimum route from the start node S to the end node E and a label attached to an arc indicating a transition between the nodes. Information is generated and output as a speech recognition result. Here, the route history information indicates a word given as a label to an arc on the route.

As a search method, for example, as shown in Reference Document 1 below, there is a technique (token passing) using token propagation on a search network.
(Reference 1) J. et al. Young, N .; H. Russell, andnd J. et al. H. S. Thornton, “Token Passing: a Conceptual Model for Connected Speech Recognition Systems, INFEDNG / TR38, Cambridge9

  In the token passing method, as illustrated in FIG. 3, the route search unit 105 performs an arc on the route from the start node S to the node n for the node n in the search network that can be reached at time t. Token token (t) having path history information hist (t, n) indicating a word given to and a cumulative score list cumscr (t, n) that accumulates and holds acoustic scores respectively corresponding to a plurality of acoustic models , n). The route search unit 105 searches the optimum route by updating the route history information and the accumulated score list by propagating this token to a node passing through the route in the search network. In the figure, an example is shown in which the token is propagated from the start node S to the nodes 1, 2 and 4 at the time t of “t = 2”. Note that the cumulative score list cumscr (t, n) is represented by an M-dimensional vector by, for example, the following Expression 3.

In Equation 3, S _m ^{(t, n)} (1 ≦ _m ≦ M) is an acoustic score accumulated corresponding to the mth acoustic model for the path from the start node S to the node n at time t. is there. The route history information and the accumulated score list need only be able to refer to the tokens corresponding to each token, and are not limited to being stored in the token itself. Therefore, for example, a method may be considered in which path history information and cumulative score lists regarding all tokens are stored in a storage area having a main storage unit such as a RAM, and each token refers to them.

Specifically, the route search unit 105 generates all token tokens (t-1, t1) having route history information hist (t-1, k) and a cumulative score list cumscr (t-1, k) at time t-1. Using the set V _t−1 of k), the token token (t, i) of the node i at the time t is obtained, and the optimum route from the start node S to the node i at the time t is obtained. The set V _t-1 represents a set of tokens that a node that passes through the node i immediately before the node i in the route from the start node S to the node i and that can reach the time t-1 has When pruning processing (optimal route candidate narrowing down) is performed in the search for the optimum route, it means a set of tokens that remain without being pruned.

In order to obtain the optimum route from the start node S to the node i at the time t, the route search unit 105 firstly, among all tokens included in the token set V _t−1 , a token that can transit to the node i at the time t. Select the set V ^{→ (t, i)} . Next, the route search unit 105 selects all cumulative score lists cumscr (t−1, k) (where k is token (t−1, k) ∈V ⁾ held by the selected token set V ^{→ (t, i). → (} all node numbers satisfying ^{(t, i)} ) and the sum (cumulative) of the values corresponding to the M acoustic models of the transition score list trans (k, i) represented by the above-mentioned formula 2 Of the obtained scores, the node having the maximum score is selected as the optimum node j ^{* according} to Equation 4.

Then, the route search unit 105, the optimum node j ^* optimum corresponding token to the token ^{token (t-1, j *} ) is propagated to node i as the cumulative score list of best token ^{token (t-1, j *} ) Using cumscr (t-1, j ^* ), transition score list trans (j ^* , i), and acoustic score list scr (t, i) for the acoustic feature extracted at node i at time t, The cumulative score list cumscr (t, i) of the token token (t, i) is updated by Expression 5.

The route history information hist (t, i) included in the token token (t, i) is expressed by Equation 6 when the label “w” is assigned to the arc representing the transition from the optimal node j ^* to the node i. Thus, it is obtained by adding “w” to hist (t−1, j ^* ).

If no label is given to the arc representing the transition from the optimal node j ^* to the node i, hist (t−1, j ^* ) is directly used as hist (t, j, It is obtained by substituting into i).

  The route search unit 105 performs these processes at all times t (t = 1, 2,..., T) up to the time (end time) “T” when the voice input ends, and “t = T”. The route history information hist (T, E) possessed by the token token (T, E) corresponding to the terminal node E at time t is output as indicating the optimum route. This is a speech recognition result.

When labels are assigned to all nodes in the search network, the optimum route obtained by the above method means a sequence of nodes that pass through the optimum route. In the example of FIG. 3, a word label is given to an arc representing a transition from a certain node to a certain node. However, when the minimum unit handled in the optimum route is a word, the state of each phoneme in each word It is not necessary to explicitly find the optimum route, and it is sufficient to find the optimum route in units of words. In such a case, it is the same as the route history information of the token token (t, i) obtained by propagating the token token (t-1, j ^* ) corresponding to the optimal node j ^* at time t-1. There may be a plurality of token tokens (t−1, k) having the path history information. When such a token set is V ^{* → (t, i)} , the path search unit 105 has values corresponding to M acoustic models in the path from the start node S to the node i at time t. Among them, the maximum value is selected, and using this, the cumulative score list cumscr (t, i) of the token token (t, i) corresponding to the node i is updated. Specifically, the route search unit 105 updates the cumulative score list cumscr (t, i) using Equation 8 shown below instead of Equation 5.

Note that k is all node numbers satisfying token (t−1, k) ∈V ^{* → (t, i)} . However, when the route search unit 105 updates the accumulated score list using Equation 8, the route history information hist (t, i) included in the token token (t, i) is expressed by Equation 6 or Equation for convenience. It is desirable to update using 7. Further, the route search unit 105 performs pruning according to the pruning condition. Specifically, for example, the pruning condition is that all values calculated corresponding to a plurality of acoustic models in the cumulative score list corresponding to each token are below a threshold value. The route search unit 105 removes tokens that satisfy such a pruning condition. The pruning condition may be, for example, that a value calculated corresponding to at least one acoustic model in a cumulative score list corresponding to each token is below a threshold value. The route search unit 105 performs pruning by removing values that satisfy such a pruning condition from the cumulative score list.

  Next, the procedure of the speech recognition process performed by the speech recognition apparatus 100 according to the present embodiment will be described with reference to FIG. The voice recognition apparatus 100 receives an input of an analog voice signal representing the voice input to the voice input unit by the function of the voice input receiving unit 101, and converts it into a digital voice signal (step S1). The speech recognition apparatus 100 extracts the acoustic feature amount for each time using the speech signal converted into digital in step S1 by the function of the acoustic feature amount extraction unit 102 (step S2). The speech recognition apparatus 100 uses the function of the route search unit 105 to detect the acoustic feature amount extracted in step S 2, the plurality of acoustic models stored in the acoustic model storage unit 103, and the search stored in the search network storage unit 104. The optimum route is searched using the network (step S3).

  FIG. 5 is a flowchart showing a detailed procedure of the process in step S3 of FIG. The route search unit 105 performs route history information hist () indicating the word given to the arc on the route from the start node S to the node n at time t as a route at time t where “t = 0” as an initial process. t, n) and a cumulative score list cumscr (t, n) that accumulates and holds acoustic scores corresponding to a plurality (M) of acoustic models are generated (step S10). Note that “t = 0” is an appropriate time before the voice is input, and the node n at the time t is the start node S. For this reason, there is no word indicated by the route history information, and the values of the cumulative score list cumscr (t, n) are all “0”.

When the time t is incremented by “1” (step S11), the route search unit 105 selects a node i that can reach the time t. Then, the route search unit 105 updates the route history information hist (t, i) to indicate the word sequence given to the arc representing the transition between the nodes passing through the route from the starting node S to the node i. The accumulated score list is updated to generate a token at time t (step S12). Next, the route search unit 105 determines whether route history information possessed by all tokens that can be propagated to the node i that can reach the time t is different (step S13). When the determination result is affirmative (step S13: YES), the route search unit 105 determines the route history information hist (t-1, k) and the cumulative score at time t-1 for the node i at time t. The token token (t, i) of the node i at time t is obtained using the set V _t-1 of all token tokens (t-1, k) having the list cumscr (t-1, k). At this time, the route search unit 105 calculates the cumulative score list cumscr (t, i) of the token corresponding to the node i using the above-described Expression 5 (step S14). For example, for the node 5 with “i = 5”, as illustrated in FIG. 6, when the acoustic scores are calculated corresponding to the three acoustic models, the accumulated tokens of the node 4 have. A score list is calculated as a cumulative score list cumscr (t, 5) corresponding to node 5. Thereafter, the process proceeds to step S16.

On the other hand, when the determination result of step S13 is negative (step S13: NO), the route search unit 105 performs route history information hist (t-1, k at time t-1 with respect to the node i at time t. ) And the set V _t−1 of all token tokens (t−1, k) having the cumulative score list cumscr (t−1, k), the token token (t, i) of node i at time t is Ask. At this time, the route search unit 105 calculates the cumulative score list cumscr (t, i) of the token corresponding to the node i at the time t using the above-described Expression 8 (step S15). For example, for the node 3 with “i = 3”, as illustrated in FIG. 7, when the acoustic scores are calculated corresponding to the three acoustic models, the first acoustic score is The value of the acoustic score of node 2 is selected, the value of the acoustic score of node 3 is selected for the second acoustic score, the value of the acoustic score of node 3 is selected for the third acoustic score, A cumulative score list cumscr (t, 3) is calculated. Thereafter, the process proceeds to step S16.

  In step S <b> 16, the route search unit 105 updates the route history information of the node i at time t using Equation 6 or Equation 7. Then, the route search unit 105 determines whether or not the token corresponding to the node i at the time t matches the pruning condition (step S17). When the determination result is affirmative (step S17: YES), the route search unit 105 removes the token (step S19), proceeds to step S18, and when the determination result is negative (step S17). : NO), the process proceeds to step S18. In step S18, the route search unit 105 determines whether there is a node other than the node i that can be reached by time t. If the determination result is affirmative (step S18: YES), the process proceeds to step S12. Then, the process is performed on the nodes other than the node i in the same manner as described above. On the other hand, when the determination result in step S18 is negative (step S18: NO), the route search unit 105 determines whether or not the time t has reached the end time “T” (step S20). If the determination result is negative (step S20: NO), the process proceeds to step S11. If the determination result is affirmative (step S20: YES), the route search unit 105 sets the time “t = T”. The route history information held by the token corresponding to the terminal node E at t is output (step S21). The route indicated by this route history information is the optimum route, and shows the recognition result of the speech that was accepted in step S1.

  As described above, according to the speech recognition apparatus according to the present embodiment, the optimal acoustic score is calculated by calculating the accumulation of acoustic scores respectively corresponding to a plurality of acoustic models on a single search network using the plurality of acoustic models. By searching for a route, it is possible to perform speech recognition with high robustness while suppressing an increase in the calculation cost and the amount of memory used for the calculation.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. Moreover, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, the constituent elements over different embodiments may be appropriately combined.

  In the embodiment described above, various programs executed by the speech recognition apparatus 100 may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. The various programs are recorded in a computer-readable recording medium such as a CD-ROM, a flexible disk (FD), a CD-R, and a DVD (Digital Versatile Disk) in a file in an installable or executable format. The computer program product may be provided.

In the embodiment described above, the method by which the route search unit 105 searches for the optimum route is not limited to the above-described example, and for example, a method using the dynamic programming shown in Reference 2 may be used.
(Reference 2) Rabiner and b. -H. Juang, “Fundamentals of Speech Recognition”, Prentice Hall Signal Processing Series, pp. 339-342, 1993.

  An example of searching for the optimum route on the search network according to this modification will be described with reference to FIG. In the figure, the horizontal axis direction represents nodes, the vertical axis direction represents time, circles in the figure represent states in the search network, and arrows represent state transitions. Each state (t, n) in the search network corresponds to node n at time t. The state (0, S) corresponding to the start node S at time t where “t = 0” is referred to as the initial state, and the state (T, E) corresponding to the end node E at time T when the voice input ends is ended. Call the state. Each state includes path history information hist (t, n) indicating a path from the initial state (0, S) to the state (t, n), and an acoustic score corresponding to a plurality of acoustic models represented by Equation 9. Has a cumulative score list cumscr (t, n) that holds.

  However, the route history information and the cumulative score list of each state need only be able to refer to those corresponding to each state, and are not limited to being held in the state itself. Therefore, for example, a method of storing path history information and cumulative score lists regarding all states in a storage area having a main storage unit such as a RAM and referring to them by each state may be considered.

The route search unit 105 sets a set Q _{t− of the} states (t−1, k) having the route history information hist (t−1, k) and the cumulative score list cumscr (t−1, k) at time t−1. ₁ is used to obtain the optimum route in the state (t, i) corresponding to the node i at time t. The set Q _t−1 is a state that passes through the state (t, i) immediately before the state (t, i) in the path from the initial state (0, S) to the state (t, i) at the time t, and includes the time t− This represents a set of states that can reach 1, and when pruning processing is performed in the search for the optimum route, it means a set of states that remain without being pruned.

In order to obtain the optimum route from the initial state (0, S) to the state (t, i) corresponding to the node i at time t, the route search unit 105 first sets the state set Q _{t-1 at} time t-1. Among all states included in Q, a set of states Q ^{→ (t, i)} that can transition to the state ^{(t, i) at the time} is selected. Next, the route search unit 105 selects all cumulative score lists cumscr (t−1, k) of the selected state set Q ^{→ (t, i} ) (where k is (t−1, k) ∈Q ^{→ (t , i),} and all the scores obtained from the sum (cumulative value) of the values corresponding to the M acoustic models of the transition score list trans (k, i) represented by Equation 2, The node with the maximum score is selected as the optimal node j ^{* according} to Equation 10.

Then, the route search unit 105 determines the cumulative score list cumscr (t-1, j ^* ) in the state (t-1, j ^* ) corresponding to the optimum node j ^* at time t-1, and the transition score list trans Using (j ^* , i) and the acoustic score list scr (t, i) corresponding to the node i at time t, the cumulative score list cumscr (t, i) in the state (t, i) is obtained according to Equation 11. Calculate

The route history information hist (t, i) possessed by the state (t, i) is expressed as shown in Expression 12 when the label “w” is given to the transition from the optimal node j ^* to the node i. , Hist (t-1, j ^* ) is obtained by adding “w”.

If no label is assigned to the transition from the optimal node j ^* to the node i, hist (t−1, j ^* ) is directly substituted for hist (t, i) as shown in Expression 13. Is required.

  The route search unit 105 performs these processes at all times t (t = 1, 2,..., T) up to the time (end time) “T” when the voice input ends, and “t = T”. The route history information hist (T, E) of the end state (T, E) corresponding to the terminal node E at time t is output as indicating the optimum route. This is a speech recognition result.

When labels are assigned to all nodes in the search network, the optimum route obtained by the above method means a sequence of nodes that pass through the optimum route. When a word label is given to a transition from a certain node to a certain node, and the smallest unit handled in the optimum route is a word, it is necessary to explicitly obtain the optimum route such as the state of each phoneme within each word. What is necessary is just to obtain the optimal route in units of words. In such a case, the same route history information as the state history (t, i) obtained by propagating the state (t-1, j ^* ) corresponding to the optimum node j ^* at time t-1 There may be a plurality of states (t−1, k) having history information. When such a set of states is defined as Q ^{* → (t, i)} , the route search unit 105 performs M sound in the route from the initial state (0, S) to the state (t-1, k). Among the values corresponding to the models, the maximum value is selected, and this is used to update the cumulative score list cumscr (t, i) of the token (t, i) corresponding to the node i at time t. Specifically, the route search unit 105 updates the cumulative score list cumscr (t, i) using the following equation 14 instead of the equation 11.

Note that k is all node numbers satisfying (t−1, k) ∈Q ^{* → (t, i)} . However, if the route search unit 105 updates the accumulated score list using Equation 14, the route history information hist (t, i) that the state (t, i) has is expressed by Equation 12 or Equation 13 for convenience. It is desirable to update using Further, the route search unit 105 performs pruning according to the pruning condition. Specifically, for example, the pruning condition is that all values calculated corresponding to a plurality of acoustic models in the cumulative score list corresponding to each state are below a threshold value. The route search unit 105 removes the state itself that satisfies such a pruning condition. The pruning condition may be, for example, that a value calculated corresponding to at least one acoustic model in a cumulative score list corresponding to each state falls below a threshold value. The route search unit 105 performs pruning by removing values that satisfy such a pruning condition from the cumulative score list.

  The procedure of the speech recognition process performed by the speech recognition apparatus 100 according to this modification is the same as that shown in FIG. The processing procedure of step S3 is substantially the same as that shown in FIG. 5, but in this modified example, the route history information and cumulative score list of the state are used instead of the route history information and cumulative score list of the token. This is different from the first embodiment described above in that the calculation is performed. The calculation method itself is the same as that of the first embodiment.

  Even with the configuration as described above, the calculation cost or the calculation cost can be reduced by calculating the accumulation of the acoustic scores corresponding to the plurality of acoustic models on the single search network using the plurality of acoustic models and searching for the optimum route. It is possible to perform speech recognition with high robustness while suppressing an increase in the amount of memory used for calculation.

  In the embodiment and the modification described above, the route search unit 105 may not perform pruning.

  In the embodiment and the modification described above, the processing performed by the route search unit 105 when a word is assigned as a label to a transition from a certain node to a certain node has been described. The route search unit 105 performs the same processing even when a word is added.

  In the embodiment and the modification described above, the route search unit 105 calculates the acoustic score corresponding to the node. However, the present invention is not limited to this, and the acoustic score is calculated corresponding to the arc. Also good.

DESCRIPTION OF SYMBOLS 100 Speech recognition apparatus 101 Voice input reception part 102 Acoustic feature-value extraction part 103 Acoustic model memory | storage part 104 Search network memory | storage part 105 Path | route search part

Claims (7)

A first storage unit for storing a plurality of acoustic models having the same structure;
A second storage unit that stores a search network common to a plurality of the acoustic models, including a start node that represents a start point, a terminal node that represents a terminal end, and at least one node between the start node and the terminal node ,
A reception unit for receiving voice input;
An extraction unit for extracting an acoustic feature using the voice;
Using the acoustic feature quantity, the plurality of acoustic models, and the search network, for each path indicating a sequence of nodes that pass from the start node to the end node on the search network, A calculator that calculates a score corresponding to each of the plurality of acoustic models;
The first route that is the optimum route from the start node to the end node on the search network by selecting the first route having the maximum score corresponding to at least one acoustic model among the routes. A search unit for searching for a route;
A speech recognition apparatus comprising: an output unit that outputs information indicating the first route, which is the speech recognition result. The first storage unit stores the acoustic model that is configured by a hidden Markov model and has a common number of states of the hidden Markov model and a structure of state transition corresponding to the plurality of acoustic models. The speech recognition apparatus according to claim 1. The calculation unit uses the acoustic feature quantity, the plurality of acoustic models, and the search network, for each of the plurality of acoustic models for each node that passes from the start node to the end node. The first score is calculated corresponding to the first route, and the first score calculated for each node passing through the first route from the starting node to the node is accumulated. Calculating the score corresponding to each of the plurality of acoustic models;
The search unit sequentially searches for an optimum route from the start node to the end node for each node passing from the start node to the end node, and reaches from the start node to the end node. Corresponds to at least one acoustic model for each second route that reaches all the second nodes that reach the first node, and that is a node that passes one node before the first node 2. The optimal route from the start node to the first node via the third route is selected by selecting a third route that maximizes the score. Voice recognition device. The calculation unit uses the acoustic feature quantity, the plurality of acoustic models, and the search network, for each of the plurality of acoustic models for each node that passes from the start node to the end node. The first score is calculated corresponding to the first route, and the first score calculated for each node passing through the first route from the starting node to the node is accumulated. Calculating the score,
The search unit sequentially searches for an optimum route from the start node to the end node for each node passing from the start node to the end node, and reaches from the start node to the end node. If there is one second route that is a route to the first node that is a node that passes through until the first route, the score calculated corresponding to each of the plurality of acoustic models for the second route, Among the scores calculated for each of the plurality of acoustic models for the third path from the starting node to the second node that passes immediately before the first node, for each acoustic model Search for the second route which is the optimum route from the starting node to the first node by updating the selected score to the score for the second route. Speech recognition apparatus according to claim 1, characterized in Rukoto. A label that is at least one of a phoneme and a word is given to all or a part of at least one of the nodes and an arc representing a transition between the nodes,
The output unit includes a series of labels attached to all or a part of at least one of a node passing from the start node to the end node in the first route and an arc representing a transition between the nodes. The speech recognition apparatus according to claim 1, wherein the information indicating the information is output. A first storage unit that stores a plurality of acoustic models having the same structure; a start node representing a start end; a termination node representing a termination; and at least one node between the start node and the termination node; A speech recognition method executed by a speech recognition apparatus including a second storage unit that stores a search network common to the plurality of acoustic models, a reception unit, an extraction unit, a calculation unit, a search unit, and an output unit Because
The accepting unit accepting voice input;
The extraction unit using the voice to extract an acoustic feature;
The calculation unit uses the acoustic feature quantity, the plurality of acoustic models, and the search network, and indicates a sequence of nodes that pass from the start node to the end node on the search network. Calculating a score corresponding to each of the plurality of acoustic models for a path;
An optimal route from the start node to the end node on the search network by the search unit selecting a first route having the maximum score corresponding to at least one acoustic model among the routes. Searching for the first route which is
And a step of outputting the information indicating the first route, which is a recognition result of the voice, to the output unit. A first storage unit that stores a plurality of acoustic models having the same structure; a start node representing a start end; a termination node representing a termination; and at least one node between the start node and the termination node; A computer having a speech recognition device comprising: a second storage unit storing a search network common to a plurality of the acoustic models;
Receiving means for receiving voice input;
Extraction means for extracting an acoustic feature using the voice;
Using each of the extracted acoustic feature quantity, the plurality of acoustic models, and the search network, each path indicating a sequence of nodes that pass from the start node to the end node on the search network. On the other hand, calculation means for calculating a score corresponding to each of the plurality of acoustic models,
The first route that is the optimum route from the start node to the end node on the search network by selecting the first route having the maximum score corresponding to at least one acoustic model among the routes. Search means for searching for a route;
The program for functioning as an output means which outputs the information which shows the said 1st path | route which is the said speech recognition result.

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