JP2009047929A - Learning method of error correction model, device, program and recording medium with the program recorded thereon - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To perform learning of an error correction model which contributes to improvement in accuracy of the error correction model. <P>SOLUTION: Error correction model learning includes repetition of: performing error correction on a set of a plurality of recognition results by pattern recognition by using an index (a score index) depending on a score of each recognition result, weighting (score index weighting) to the score index, a feature quantity of each recognition result and weighting (feature quantity weighting) for the feature quantity (S104); and updating the feature quantity weighting by using an error correction result obtained by the error correction (S105), wherein the recognition result set is a subset which is included in the set comprising the plurality of recognition results obtained by the pattern recognition, and the set of high recognition error rate(S103). <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to learning of error correction models used for pattern recognition and the like.

  In pattern recognition such as speech recognition, handwritten character recognition, and language translation, one or more recognition results are presented in the order of score for input data. Generally, the recognition result is the result with the highest score. However, in the case where real-time processing is not required, re-assignment of a score using an error correction model is performed on all results, and a recognition result with a lower recognition error rate is obtained.

  For example, when a word string is output as a recognition result as in speech recognition, there is a method of re-assigning a score according to a word arrangement (word N-gram) as one method of error correction. In this error correction model learning, a score corresponding to the sequence of each word is estimated. A high score of words obtained from the correct word string is given, and a small score of words obtained from other word strings is given. In the past, due to the nature of the problem of error correction, it has been important to give a small value to the word sequence obtained from a word string having a higher score than the correct answer.

As such an error correction learning method in the language processing field, for example, Non-Patent Document 1 is cited. The method disclosed in Non-Patent Document 1 learns an error correction model by a perceptron algorithm using N-gram frequency for each hypothesis that is a word string of a recognition result as a feature.
Non-patent document 2 can also be cited as an error correction learning method in the natural language processing field.
Brian Roark, Murat Saraclar, Michael Collins, “Corrective Language Modeling for Large Vocabulary ASR with the Perceptron Algorithm,” Proceedings of ICASSP, vol.1, pp.749-752, 2004. Zhengyu Zhou, Jianfeng Gao, Frank K. Soong and Helen Meng, “A Comparative Study of Discriminative Methods for Reranking LVCSR Hypotheses in Domain Adaptation and Generalization,” Proc. ICASSP, vol.1, pp.141-144, 2006.

  In conventional error correction model learning, emphasis is placed on reducing the score of a recognition result having a higher score than the correct answer, and an error pattern has not been sufficiently acquired. This adversely affects the accuracy of the error correction model.

  In view of the above problems, an object of the present invention is to learn an error correction model that contributes to improving the accuracy of the error correction model.

In order to solve the above-described problem, in the present invention, an index (hereinafter referred to as a score index) that depends on the score of each recognition result for a set of a plurality of recognition results by pattern recognition (hereinafter referred to as a recognition result set) The error correction is performed using the weight for the score index (hereinafter referred to as the score index weight), the feature amount of each recognition result, and the weight for the feature amount (hereinafter referred to as the feature amount weight). Error correction model learning including updating feature weights using an obtained error correction result, and a recognition result set is a set of a plurality of recognition results obtained by pattern recognition (hereinafter referred to as set A). Is a set consisting of the subsets having a high recognition error rate.
Conventionally, learning is performed using all recognition results included in the set A, but in the present invention, the recognition results used for learning are limited based on the recognition error rate criterion. For this reason, the influence of the recognition result with few errors is excluded, and the learning of the error correction model in which the influence of the recognition result including many errors is strongly reflected is realized. As a result, efficient acquisition of error patterns can be expected.

  In consideration of the convergence of learning, the recognition result set may include a correct pattern recognition answer.

Further, the calculation result obtained by weighting the score index by the score index weight (calculation result A) and the calculation result obtained by weighting the feature quantity by the feature quantity weight (calculation result) obtained according to each recognition result included in the recognition result set. Regarding the sum of B), the score index weight may be set so that the calculation result in which the feature amount is weighted by the feature amount weight is dominantly determined as the magnitude relationship between them. If the magnitude relationship of the sum of the calculation result A and the calculation result B is dominantly determined by the calculation result A, the error pattern may not be acquired sufficiently. Prevent this.
Alternatively, the score index weight may be set so that the calculation result obtained by weighting the score index with the score index weight has an inverse correlation with the score of the recognition result.

  If the pattern recognition is to obtain a word string as a recognition result, the feature amount is used as an index representing the characteristics of the word string based on the word sequence, and the recognition result set is obtained by pattern recognition to obtain the word string as the recognition result. It is a subset that is included in a set of a plurality of recognition results, and can be a set that has a high word error rate or a low score.

  The computer can be operated as an error correction model learning device by a program that causes the computer to execute each process of the error correction model learning method of the present invention. A computer-readable program recording medium that records this program makes it possible for another computer to function as an error correction model learning device, to distribute the program, and the like.

  According to the present invention, since an error pattern is sufficiently acquired, a more accurate error correction model is generated. In addition, since the recognition result set used for learning is a subset of recognition results limited by the recognition error rate criterion, it contributes to the generation of a compact error correction model.

"theory"
Prior to the description of the embodiment, the theory of error correction model learning of the present invention will be described. The present invention can be applied to general error correction model learning of pattern recognition. However, in order to make the description concrete, the description will be made with speech recognition as an example. First, features and feature vectors will be described. After that, an outline of error correction by rescoring and a parameter learning method of an error correction model will be described, and then the essential points of the present invention will be described.

[Feature]
When a score is given to the recognition result of pattern recognition, it is necessary to express what characteristics each recognition result has. An index representing this feature is a feature amount. Various features can be arbitrarily selected as the feature amount. The feature amount is generally expressed as a vector, but includes a scalar in the sense of a one-dimensional vector.

  For example, in speech recognition, when a score is given to a word string that is a recognition result, it is necessary to express what characteristics each word string has. That is, it is necessary to define the feature amount of the word string. In order to realize this, the concept of “feature” is introduced. A feature is generally understood as a collection of attributes and attribute values. The vector of attribute values is called a feature vector, and this is a feature quantity.

  Designers have discretion in how to define the features, that is, how to give attributes and attribute values. Here, for convenience of explanation, the word N-gram and the feature using the frequency are mentioned as an example.

A word N-gram is a sequence of N words. Now, if I'm always drinking milk in the word sequence, the 1st-gram in the third position with the word morning is 'morning', 2-gram is 'morning +', and 3-gram is 'me' + Becomes + morning. Here, each of these is an attribute. If the frequency at which each word N-gram is observed in the word string is defined as the frequency, the frequency corresponding to the above three attributes is 1 and these are attribute values corresponding to the respective attributes. On the other hand, the attribute value of the attribute that does not exist in the word string such as 2-gram “I + morning” is 0.

  As described above, when the word N-gram and its frequency are used for the feature, the feature vector corresponds to an array of frequencies corresponding to each word N-gram. When only the word 1-gram is used, the length of the feature vector can be considered as the size of the presupposed vocabulary. For example, assuming a maximum vocabulary size of 10,000 words, the feature vector obtained from the above word sequence is the word “I” is “morning” and “milk” is “drink”. The component is only 1 at 7 locations corresponding to 1-gram, and the components at other 9993 locations are 0.

  Since N of the word N-gram is the number of connected words, the length of the feature vector is on the order of the vocabulary size to the Nth power. However, since the region for the feature acquired from the learning data is actually sufficient, the vector length is suppressed and the size can be stored in a memory device of a general computer.

[Error correction by rescoring]
A set of recognition results output by the pattern recognition processing for the kth input data is denoted as Hyps _k , and the nth recognition result is denoted as w _{k, n} . Recognition result w _k, the recognition score of the _n P _{k, n,} recognition score P _k, an index that depends on _n (the score indicator) [Phi (P _{k, n)} to the time, the recognition result w _k, the feature amount of _n φ (w _{k, n),} the feature quantity φ (w _{k, n)} weight alpha, score indicator Φ (P _{k, n)} to, and score index Φ (P _{k, n)} using the weight λ to formula Error correction is realized by (1). The function f is a function that receives the score index Φ (P _{k, n} ) and the weight λ, and returns a calculation result in which the score index Φ (P _{k, n} ) is weighted by a predetermined calculation, and the function g is a feature This is a function that receives an amount φ (w _{k, n} ) and a weight α and returns an operation result in which the feature amount φ (w _{k, n} ) is weighted by a predetermined operation. Each of the score index Φ (P _{k, n} ) and the score index weight λ is generally expressed as a vector, but also includes a scalar in the sense of a one-dimensional vector. The functions f and g are normally set to return a scalar value as output. In equation (1), f (Φ (P _{k, n} ), λ) depends on the recognition score, and g (φ (w _{k, n} ), α) is independent of the recognition score.

For example, in the case of speech recognition, the hypothesis set of speech recognition for the k-th utterance data is Hyps _k , and the n-th hypothesis is w _{k, n} . A hypothesis may be considered as a word string output by speech recognition. Hypothesis w _k, score indicator Φ (P _{k, n)} of the _n the logP _k, when _{n, the} hypothesis w _k, feature vector φ (w _{k, n)} of _n and the weight vector alpha, and score indicator [Phi ( Error correction is realized by equation (2) using the weight λ to P _{k, n} ) logP _{k, n} . The symbol • represents the inner product.

The hypothesis set Hyps _k may be an N-best hypothesis list output by speech recognition processing (see Non-Patent Document 1 for the case of using a network representation of a word string called a word lattice). Here, N-best is the top N recognition results in score order. N here has nothing to do with N in the word N-gram.
α is a parameter of the error correction model estimated by learning. Next, this learning method will be described.

[Learning parameters of error correction model]
For learning, discriminative learning techniques such as perceptron algorithm, conditional random field, and Minimum Sample Risk are often used. In either case, the policy is to give a high score to the feature value obtained from the correct answer and give a low score to the other. In the example of speech recognition, a policy is adopted in which a high score is given to a feature obtained from a correct word string, and a low score is given otherwise.

Here, a perceptron algorithm will be described as an example of an α learning method (see Non-Patent Document 1). Prior to learning, α is initialized to a zero vector. λ is set to an arbitrary constant, and is generally given a positive value. Then, Equation (2) is applied to k = 1st data to determine a hypothesis w ^* _k . Since α is a zero vector at the beginning of learning, this hypothesis is determined only by the speech recognition score. Next, using this hypothesis as an alternative hypothesis, the parameter is updated by equation (3). Expression (3) means that α + φ (w _k ^ref ) −φ (w ^* _k ) is set as a new α. w _k ^ref is a correct word string, but here it is better to substitute a hypothesis (Oracle) with the lowest word error rate. It is known that using the Oracle hypothesis is a more accurate model than the true correct answer (see Non-Patent Document 1). The parameter α is learned by recursively applying the above processing formula (2, 3) to all learning data.

[Application of error correction model]
When the error correction model obtained by learning is applied, error correction is performed by applying Equation (1) to the set of recognition results output by the speech recognition processing. In the example of speech recognition, equation (2) may be applied to the N-best hypothesis list output by speech recognition processing. At this time, α obtained by learning is used. Also, λ is set to the same value as at the time of learning. Note that λ can be learned by the same procedure as α. In this case, λ obtained by learning is used.

[About the present invention]
Discrimination between a correct answer and a recognition result close to it becomes a factor of overfitting with respect to learning data, and inhibits efficient acquisition of an error pattern. However, when learning is performed according to normal procedures, identification of such recognition results tends to occupy most of the learning.
Therefore, in the present invention, the following two processes are performed in order to dominantly identify the recognition result with many errors.

The first process relates to the recognition result set Hyps _k . Usually, learning is performed using all of the recognition results included in the recognition result set Hyps _k obtained by the pattern recognition processing, but in the present invention, learning is performed using a subset limited by the recognition error rate criterion. . Specifically, a subset composed of recognition results having a high recognition error rate [the number of elements of the subset excluding correct answers may be one. ] As Hyps _k in the equation (1). This process also has the aspect of generating a small error correction model. However, for the convergence of learning, this Hyps _k has the correct answer as an element. Specifically, a feature amount error rate, a recognition score, or the like can be used as the recognition error rate criterion. If the feature amount error rate is used, a subset consisting of recognition results having a high feature amount error rate is selected. In general, a recognition result with a low recognition score is considered to have a high feature amount error rate. Therefore, if a recognition score is used, a subset consisting of recognition results with a low recognition score is selected. Similar criteria may be used in addition to the feature amount error rate and the recognition score.
In an example of speech recognition, learning is normally performed using all hypotheses included in the N-best hypothesis list, but a hypothesis set limited by a word error rate (or speech recognition score) criterion (a subset of the N-best hypothesis list) ). In other words, only hypotheses with a high word error rate are used in learning. However, for use, Hyps _k has a correct word string as an element for convergence of learning.

The second process includes an operation result f (Φ (P _{k, n} ), λ) obtained by weighting the score index Φ (P _{k, n} ) by the score index weight λ obtained according to each recognition result, For the sum of the calculation result g (φ (w _{k, n} ), α) in which the feature quantity φ (w _{k, n} ) is weighted by the feature quantity weight α, the magnitude relationship between these is calculated as the calculation result g (φ The score index weight λ is set so that (w _{k, n} ), α) is dominantly determined.

The magnitude relationship of f (Φ (P _{k, n} ), λ) + g (φ (w _{k, n} ), α) obtained for each recognition result w _{k, n} included in the subset is the operation result f (Φ ( P _{k, n),} λ) dispersion calculation result of _{g (φ (w k, n} ), unless sufficiently than the dispersion of the alpha) smaller, the operation result _{f (Φ (P k, n} ), λ) Depends on.
In such a case, the magnitude relationship of f (Φ (P _{k, n} ), λ) + g (φ (w _{k, n} ), α) for each recognition result is the calculation result f (Φ (P _{k, n} ), λ ) Is set so as to be almost independent of). This means that the degree of contribution of the calculation result f (Φ (P _{k, n} ), λ) to the magnitude relationship is reduced, and is determined by the correlation with the calculation result g (φ (w _{k, n} ), α). It is a design matter.

  For example, when the magnitude relationship is given by Equation (2), the score index weight λ (here, a scalar) may be zero or a positive value close thereto. When the value of λ is zero, the influence of the recognition score is excluded. Generally, there is a bias in recognition scores between correct answers (Oracle) and other recognition results with a high recognition error rate. That is, the overall recognition score is high for the former and low for the latter. For this reason, when the first process is applied, discrimination is mostly achieved only by the recognition score, and learning may converge immediately and acquisition of a sufficient error pattern may not be achieved. The second process prevents insufficient learning from the first process. Further, the second process has an effect of promoting discrimination from a recognition result with many errors even by itself.

As an alternative to the second process, the score index weight λ is set so that the calculation result f (Φ (P _{k, n} ), λ) in equation (1) has an inverse correlation with the recognition score P _{k, n} You can also This achieves the purpose of dominantly discriminating the recognition result having many errors by utilizing the property that the recognition score P _{k, n} generally has a negative correlation with the recognition error rate.

For example, in Equation (2), λ (here, a scalar) may be set to a negative value. Since the inverse correlation is given to the recognition score P _{k, n} having a negative correlation with the recognition error rate _, λ log P _{k, n} has a positive correlation with the recognition error rate as a result. For this reason, it is possible to perform parameter estimation with an emphasis on recognition results with a high recognition error rate.

  Note that both means of the second process can be expected to have a certain effect even when the first process is not applied. In other words, it is possible to achieve to some extent the purpose of dominantly distinguishing from recognition results with many errors. In general, λ can be learned in the same way as α. If this is desired, the initial value of λ is set as described above, and the second process is performed by updating λ very finely. Realized.

The present invention is applied at the time of learning an error correction model, and when applying a learned error correction model (at the time of analysis), a normal process may be performed. In other words, all recognition results included in the recognition result set are used for Hyps _k . λ is set to a positive value, and error correction processing is executed based on Equation (1). In the speech recognition example, all hypotheses in the N-best hypothesis list are used for Hyps _k . λ is set to a positive value, and error correction processing is executed based on Equation (2).

  Thus, in the present invention, it should be noted that a value different from the value used at the time of analysis is set as λ at the time of learning. Also in this point, there is a difference from the conventional technique and it can be positioned as a feature of the present invention. However, as described above, this is not the case when λ is learned as in α.

<Embodiment>
Hereinafter, embodiments of the error correction model learning apparatus and method will be described. For the sake of specific explanation, a case where speech recognition is taken as an example of pattern recognition and a perceptron algorithm is adopted as a learning algorithm will be described. The error correction model learning apparatus is realized by a computer such as a dedicated machine configured by dedicated hardware or a general-purpose machine such as a personal computer. Here, a description will be given of a case where it is realized by a computer (general-purpose machine).

A hardware configuration example of the error correction model learning device will be described.
The error correction model learning device includes an input unit to which a keyboard and a pointing device can be connected, an output unit to which a liquid crystal display, a CRT (Cathode Ray Tube) display, and the like can be connected, and communication capable of communicating outside the error correction model learning device. A communication unit to which devices (for example, a communication cable, a LAN card, a router, a modem, etc.) can be connected and a CPU (Central Processing Unit) [DSP (Digital Signal Processor) may be used. A cache memory, a register, or the like may be provided. ], RAM, ROM, which is a memory, an external storage device such as a hard disk, an optical disk, a semiconductor memory, etc., and the exchange of data between these input unit, output unit, communication unit, CPU, RAM, ROM, external storage device It has a bus that connects as possible. If necessary, the error correction model learning device may be provided with a device (drive) that can read and write storage media such as a flexible disk, a CD-ROM (Compact Disc Read Only Memory), and a DVD (Digital Versatile Disc). .

  The external storage device of the error correction model learning device stores a program for learning an error correction model and data necessary for processing of the program [not limited to the external storage device, for example, a program is read-only stored. You may memorize | store in the ROM which is an apparatus. ]. Further, data obtained by the processing of this program is appropriately stored in a RAM, an external storage device, or the like. Hereinafter, a storage device that stores data, addresses of storage areas, and the like is simply referred to as a “storage unit”.

In this embodiment, the hypothesis set Hyps _k output by the speech recognition process for K speech data (learning data) is stored in advance in the storage unit (10). However, 1 ≦ k ≦ K. A plurality of hypotheses included in the hypothesis set Hyps _k are associated with a speech recognition score and a word error rate. The word error rate is obtained by comparing a true correct word string with a hypothesis. Further, the true correct word string is stored in advance in the storage unit (10) as necessary.

The external storage device (or ROM, etc.) of the error correction model learning device has a program for setting parameters used for learning, a program for selecting hypotheses used for learning from the hypothesis set Hyps _k, and a program for error correction In addition, a program for updating parameters and a program for performing control required by processing based on each program are stored.

  In the error correction model learning apparatus, each program and data necessary for processing each program are read into the RAM as necessary, and are interpreted and executed by the CPU. As a result, the CPU implements predetermined functions (parameter setting unit, selection unit, error correction unit, parameter update unit, control unit), thereby realizing error correction model learning.

  Next, with reference to FIG. 1 and FIG. 2, the flow of error correction model learning processing by the error correction model learning apparatus will be described descriptively.

First, the parameter setting unit (11) sets initial values of parameters (step S101). That is, the weight λ for the speech recognition score is set to the small value described above, and the weight α for the feature vector is set to a zero vector. Further, the true correct answer or the oracle word string is read from the storage unit (10), and w _k ^ref is set to either the true correct answer or the oracle word string. The set parameters are input to the error correction unit (13) and the parameter update unit (14).

  Next, the control unit (19) sets t = k = 1 (step S102). When T is an upper limit value of the number of parameter updates, 1 ≦ t ≦ T. T is set to an arbitrary value.

Subsequently, the selection unit (12) reads the hypothesis set Hyps _k from the storage unit (10) according to the set _k , and selects a subset to be used for learning on the basis of the word error rate (step S103). The selected subset includes a plurality of selected hypotheses and their speech recognition score pairs. As described above, a subset consisting of hypotheses with a high word error rate is selected. As a specific example, all hypotheses included in the hypothesis set Hyps _k have the lowest word error rate, and the word error rate is the highest. Can be rearranged so that the largest is the lowest, and hypotheses from the Mth to the lowest can be selected. Alternatively, the lower L hypotheses may be selected. In this case, it is not essential to include the lowest one. Alternatively, a hypothesis having a word error rate that is less than (less than) the average value of the word errors of all hypotheses may be selected. There is no limitation on the criteria for selecting a subset of hypotheses with a high word error rate. The selected subset is input to the error correction unit (13).

Next, the error correction unit (13) calculates w ^* _k according to equation (2) (step S104). However, Hyps _k in Equation (2) is a subset obtained by the processing in step S104. The calculated w ^* _k is input to the parameter update unit (14).

Subsequently, the parameter update unit (14) calculates α according to the equation (3) (step S105).
The control unit (19) sets k to 1 as a new value of k (step S106),
It is determined whether or not k = K (step S107). If k ≠ K, the control unit (19) performs control so that the processing after step S103 is performed using α obtained by the processing of step S105 as an input to the error correction unit (13).

  If k = K, the control unit (19) sets t to 1 as a new value of t (step S108), and determines whether t = T (step S109). If t ≠ T, the control unit (19) performs control so that the processing after step S103 is performed by using α obtained by the processing of step S105 as an input to the error correction unit (13). If t = T, the control unit (19) performs control to store α obtained in the process of step S105 in the storage unit (10) as a parameter obtained by learning, and the learning process ends.

It is empirically known that a high-accuracy error correction model can be generated by using the averaging parameter α _ave = Σ _{t, k} α _k ^t / KT when used for analysis ( See references). Here, α _k ^t is a value of α obtained by processing in the t-th loop of the k-th data.
(Reference) Michael Collins. “Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms,” Proceedings of the Conference on Empirical Methods for Natural Language Processing, pp.1-8, 2002.

In this embodiment, a hypothesis set Hyps _k output by speech recognition processing for K utterance data (learning data) is stored in advance in the storage unit (10), and the selection unit (12) determines a subset. It was something to do. However, if the subset is stored in advance in the storage unit (10), the selection unit (12) is not an essential component. Further, the parameter setting unit (11) is not an essential component as long as the initial value of the parameter is determined and stored in the storage unit (10).

  In addition to the above embodiments, the error correction model learning apparatus and method according to the present invention are not limited to the above-described embodiments, and can be appropriately changed without departing from the spirit of the present invention. In addition, the processing described in the error correction model learning apparatus / method is not only executed in time series according to the order described, but also executed in parallel or individually as required by the processing capability of the apparatus that executes the processing. It may be done.

  When the processing functions in the error correction model learning device are realized by a computer, the processing contents of the functions that the error correction model learning device should have are described by a program. By executing this program on a computer, the processing function in the error correction model learning apparatus is 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. Specifically, for example, as a magnetic recording device, a hard disk device, a flexible disk, a magnetic tape or the like, and as an optical disk, a DVD (Digital Versatile Disc), a DVD-RAM (Random Access Memory), a CD-ROM (Compact Disc Read Only). Memory), CD-R (Recordable) / RW (ReWritable), etc., magneto-optical recording medium, MO (Magneto-Optical disc), etc., semiconductor memory, EEP-ROM (Electronically Erasable and Programmable-Read Only Memory), etc. Can 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. Furthermore, 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 device. When executing the process, the computer reads a program stored in its own recording medium and executes a process according to the read program. As another execution form of the program, the computer may directly read the program from a portable recording medium and execute processing according to the program, and the program is transferred from the server computer to the computer. Each time, the 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 in this embodiment includes information that is used for processing by an electronic computer and that conforms 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 this embodiment, the error correction model learning device is configured by executing a predetermined program on the computer. However, at least a part of the processing contents may be realized by hardware. .

The effect of the present invention was verified using a Japanese spoken corpus (CSJ). The CSJ stores voice data and correct word string information for each lecture voice. First, among the lecture data included in CSJ, 2,682 lectures were divided as shown in Table 1. Furthermore, for the data set that needs to generate the N-best hypothesis list, the speech data is divided into utterance units.

  First, a language model used in speech recognition was generated using train2, and a 100-best hypothesis list for each utterance of train1 was generated by a speech recognition decoder. Next, the language model was trained using train1 and train2, and a 100-best hypothesis list for each utterance of test was generated in the same manner. In addition, the error correction model was learned under various conditions using the 100-best hypothesis list of train1, and each error correction model was applied to the 100-best hypothesis list of test to compare the word error rates.

In order to describe the details of the experiment, it is necessary to clarify the hypothesis used for the learning specifically regarding the limitation of the hypothesis used for the learning which is the first processing. Therefore, the following notation is introduced. Now, hypotheses (word strings) in the N-best hypothesis list are rearranged in ascending order based on the word error rate, and the ranking is expressed by superscripts. That is, the first hypothesis has the lowest word error rate, the Nth hypothesis has the highest word error rate, and the i-th hypothesis is expressed as w _k ⁱ . In this case, w _k ¹ = w _k ^ref because the Oracle hypothesis (the first word error rate) is the correct hypothesis. Next, a subset of the N-best hypothesis list is expressed as Hyps _k (x, y) = {w _k ⁱ : i = 1, x ≦ i ≦ y}. At this time, Hyps _k (2, N) is composed of all hypotheses in the N-best hypothesis list, and Hyps _k (N, N) is composed only of the correct hypothesis and the hypothesis having the highest word error rate.

Also, since the value of λ differs between learning and analysis, it may become ambiguous which value is mentioned. Therefore, they are written as λ ^train and λ ^test , respectively.

First, an error correction model was learned by giving different values to y = 100 and x in Hyps _k (x, y). FIG. 3 shows word error rates when these error correction models learned under λ ^train = 0 are applied to test. Before reranking in the figure shows the word error rate before applying the error correction model. Baseline shows the error correction model learned under λ ^train = λ ^test using all hypotheses in the 100-best hypothesis list. It shows the result when using and analyzing. In this case, the word error rate is only improved to about 0.2%. On the other hand, the error correction models learned under λ ^train = 0 all have improved performance from a word error rate of 16.5% to about 16.7%. A particularly interesting result is that a highly accurate error correction model can be generated by learning using only x = 100, that is, a correct answer and a hypothesis having the highest word error rate. Setting a small value for x had no effect on further accuracy improvement, but rather responded sensitively to λ ^test .

Next, FIG. 4 shows the word error rate when only y is changed with x = 2. However, learning is performed under λ ^train = 0. A high accuracy improvement rate is obtained by setting a large value for y. Also from this result, the superiority of the present invention that a hypothesis having a high word error rate is used for learning can be seen.

Finally, Table 2 compares the number of parameters of the error correction model. The number of parameters is the number of non-zero elements of α. Setting a small value to λ ^train can acquire many error patterns and generate a highly accurate error correction model, but the number of parameters increases. However, by setting a large value for x, the number of parameters can be suppressed without a decrease in accuracy.

  The present invention is useful for learning an error correction model applied to recognition results of pattern recognition such as speech recognition, handwritten character recognition, and language translation.

The function block diagram of the error correction model learning apparatus which concerns on embodiment. The processing flow of the error correction model learning process in an error correction model learning apparatus. Word error rate when each error correction model trained under λ ^train = 0 by applying different values to x in the subset Hyps _k (x, 100) is applied to test (lecture data included in CSJ) FIG. Word error rate when each error correction model trained under λ ^train = 0 by applying different values to y in subset Hyps _k (2, y) is applied to test (lecture data included in CSJ) FIG.

Explanation of symbols

11 Parameter setting unit 12 Selection unit 13 Error correction unit 14 Parameter update unit

Claims (10)

For a set of a plurality of recognition results by pattern recognition (hereinafter referred to as a recognition result set), an index (hereinafter referred to as a score index) that depends on the score of each recognition result and a weight for the score index (hereinafter referred to as a score index) An error correction step for performing error correction using a feature amount of each recognition result and a weight for the feature amount (hereinafter referred to as feature amount weight),
An error correction model learning method including a parameter update step of updating the feature weight using the error correction result obtained in the error correction step,
The recognition result set is
A learning method of an error correction model, characterized in that it is a subset that is a subset included in a set of a plurality of recognition results obtained by pattern recognition and has a high recognition error rate. 2. The error correction model learning method according to claim 1, wherein the recognition result set includes a correct answer of pattern recognition. About the sum of the calculation result obtained by weighting the score index by the score index weight and the calculation result by weighting the feature quantity by the feature quantity weight, obtained according to each recognition result included in the recognition result set. 3. The score index weight is set such that a magnitude relationship between the two is determined by a calculation result in which the feature amount is weighted by the feature amount weight. The error correction model learning method described in 1. The score index weight is set so that a calculation result obtained by weighting the score index with the score index weight has an inverse correlation with the score of the recognition result. Error correction model learning method. The pattern recognition is to obtain a word string as a recognition result,
The feature amount is an index representing the feature of the word string based on the sequence of words,
The recognition result set is
A subset included in a set of a plurality of recognition results obtained by pattern recognition that obtains a word string as a recognition result, and is a set that has a high word error rate or a low score The error correction model learning method according to claim 1. For a set of a plurality of recognition results by pattern recognition (hereinafter referred to as a recognition result set), an index (hereinafter referred to as a score index) that depends on the score of each recognition result and a weight for the score index (hereinafter referred to as a score index) Error correction means for performing error correction using a feature amount of each recognition result and a weight for the feature amount (hereinafter referred to as feature amount weight),
An error correction model learning device including an error correction result obtained by the error correction means and a parameter update means for updating the feature amount weight,
The recognition result set is
A learning apparatus for an error correction model, wherein the learning apparatus is a subset that is a subset that is included in a set of a plurality of recognition results obtained by pattern recognition and that has a high recognition error rate. About the sum of the calculation result obtained by weighting the score index by the score index weight and the calculation result by weighting the feature quantity by the feature quantity weight, obtained according to each recognition result included in the recognition result set. 7. The error according to claim 6, wherein the score index weight is set so that a magnitude relationship between the two is determined by a calculation result in which the feature value is weighted by the feature value weight. Correction model learning device. 7. The error correction model according to claim 6, wherein the score index weight is set so that a calculation result obtained by weighting the score index with the score index weight has an inverse correlation with the score of the recognition result. Learning device.       A program for causing a computer to execute each process of the error correction model learning method according to any one of claims 1 to 5.       A computer-readable recording medium on which the program according to claim 9 is recorded.

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