Speech recognition system and method

Abstract: A sound analyzer (11) sound analyzes an input speech signal to obtain feature vectors. A matrix quantizer (13) performs a matrix quantization process between the feature vectors obtained by the sound analyzer and a phonetic segment dictionary prepared in phonetic segment units to obtain a phonetic segment similarity sequence. A phonetic segment integrating section (14) integrates the phonetic segment similarity sequence into a phonemic feature vector. A HMM recognizer (15) checks the phonemic feature vector using a HMM prepared in certain units, to thereby perform a recognition process.

Description: The present invention relates to a system for and methods of speech recognition which permit utterances to be recognized with high accuracy. Recently, a speech recognition system has achieved success which employs the HMM (hidden markov model) which transforms an utterance into a sequence of certain symbols (this transformation is referred to as vector quantization) and then models the utterance as the transition of the symbol sequence. A table that is referred to in transforming the utterance into symbols is called a phonetic segment (PS) table. The HMM is represented by a transition network having more than one state, in which, for each state, the probability of occurrence of each symbol and the inter-state transition probability are embedded. When the PS dictionary uses steady coefficients (for example, spectrum coefficients or cepstrum coefficients), speech events depend only on state information embedded in the HMM (there is no time relationship in one state). For this reason, differential information, such as Δ cepstrum, is introduced. That is, a method is adopted which replaces an utterance with a symbol sequence with not only its spectrum but also its time variations taken into account. However, with the PS dictionary having a large number of dimensions, distortion introduced by quantization will inevitably become very great. For this reason, use is made of two or more PS dictionaries having their numbers of dimensions decreased by dividing a parameter space (in the above example by separating the spectrum and the time variation information). Besides those methods, there is a method which directly quantizes a sequence of spectra (or cepstrum), i.e., two-dimensional patterns, this method being called matrix quantization. The matrix quantization has, on the one hand, an advantage that speech patterns can directly be handled without approximation and, on the other hand, a drawback that quantization distortion increases. Thus, a method of decreasing the distortion by using a statistical technique at the time of quantization has been proposed. However, even if those methods are used, distortion introduced by quantizing an utterance still remains great. Thus, a means of further decreasing the distortion is desired. In order to solve distortion problems, it is necessary only that a speech spectrum (or cepstrum) be directly expressed within the HMM without replacing it with symbols (i.e., without quantizing). Such a method is called "continuous HMM" as opposed to "discrete HMM" involving quantization. In general, the continuous HMM needs huge quantities of calculations. The reason is that a covariance matrix corresponding to each state must be obtained from an input vector sequence to the HMM, and then the products of input vectors and covariance matrices must be calculated at the time of speech recognition. When an utterance is expressed by the HMM, a phoneme, a syllable, a word, a clause, or a sentence will be considered as its unit. Whatever the unit, it is important that an input utterance and its model agree well with each other at the time of recognition, in other words, distortion be as low as possible. As described above, the best is the continuous HMM which directly enters into the HMM two-dimensional patterns that contain variations of the speech spectra with time. A problem with the continuous HMM is that difficulties are involved in putting it into a practical use because huge quantities of calculations are needed. In the article "Information theory principles for the design of self-organising maps in combination with Hidden Markov Modeling for continuous speech recognition", Regoll G., International Joint Conference on Neural Networks, 17 June 1990, San Diego, USA, pages 569-574 there is introduced a design of Kohonen's self-organising maps using Information Theory principles. One particular aspect described therein is the combination of self-organising maps with Information Theory algorithms, in particular with Hidden Markov Modelling (HMM) for continuous speech recognition. Resulting from that combination is the aspect of designing the map using different rules for modifying the environment and the adaption gain during learning. It is an object of the present invention to provide a speech recognition system and method which permit a speaker's utterance to be recognized with high accuracy, and obviate the necessity of huge quantities of calculations. According to the present invention there is provided a speech recognition system and a speech recognition method as defined in claims 1 and 7, respectively. Further favourable embodiments are defined by the subclaims. The present invention sound-analyzes an input speech signal to obtain feature parameters, performs matrix quantization processing between the feature parameters and a phonetic segment dictionary of predetermined phonetic segment units to obtain a phonetic segment similarity sequence, integrates the phonetic segment similarity sequence into a phonemic feature vector, and checks the phonemic feature vector obtained by the integration using a hidden markov model (HMM) prepared in certain units, thereby performing a high-accuracy recognition process. This invention can be more fully understood from the following detailed description when taken in conjunction with the accompanying drawings, in which: Fig. 1 is a schematic block diagram of a speech recognition device to which a speech recognition system of the present invention is applied; Fig. 2 illustrates one example of a phonemic feature vector corresponding to a word part; Fig. 3 illustrates a typical example of an HMM structure; Fig. 4 is a block diagram of the PS-phoneme integrating section of Fig. 1 constructed from maximum-value filters; and Fig. 5 is a diagram illustrating the PS-phoneme integrating section of Fig. 1 constructed from neural networks. With reference to the drawings, the preferred embodiment of the present invention will be described in detail as follows. In the present invention, a phonetic segment (hereinafter abbreviated to PS) that is phonetically significant is used as a unit of quantization, and after a sequence of PS similarities (distances) is once integrated into a phoneme, a phonemic feature vector sequence is fed to an HMM word matching section. In Fig. 1, a continuous input speech signal is fed to an analog-to-digital (A/D) converter 11 and is quantized at a sampling frequency of, for example, 12 KHz to obtain a digital signal of 12 bits. The digital speech signals from A/D converter 11 are fed to a power calculation section 12a and mel-cepstrum analysis section 12b employing linear predictive coding (LPC) in digital signal processor 12. Power calculation section 12a calculates the power of input speech with each frame cycle, i.e., every 8 msec, and outputs the results of calculation to a PS-phoneme integrating and converting section 14. LPC analysis section 12b is an analysis section that employs LPC mel-cepstrum of 16 levels as analysis parameters at, for example, 16 msec for window length and 8 msec for frame cycle. The symbol sequence for each frame thus obtained with the assignment of eight levels to 16 channels are transmitted as feature parameters for the input speech to silent, buzz, voiceless extraction section and matrix quantizer 13a of digital signal processor 13. PS herein is an achronym for phonetic segment to indicate a unit for speech recognition denoting a meaning phonetically as in the following. Phonetic segments (hereinafter called as PS) employed for speech inputs in expressions of four arithmetic operations are classified into the following seventy-nine types. (1) In matrix quantizer 13a the continuous matching process by PS is performed using similarity measures based on a subspace method represented by where C(PS) denotes the LPC mel-cepstrum (C = {C1, C2, ..., CN}),  denotes the eigenvector of PS, (.) denotes the inner product, and ∥ ∥ denotes the norm. That is, PS dictionary 13b is expressed by M orthogonal eigenvectors for each PS. Here, the PS used in the present invention will be described. For example, the PS comprises: (1) A continuant segment; (1-1) a steady part of a vowel, (1-2) a continuant consonant. (2) A consonant segment; a demisyllable including a transient part to a vowel. (3) A boundary segment; (3-1) a boundary part between a vowel and a semi-vowel, (3-2) a boundary part between a vowel and a consonant, and (3-3) a boundary part between a vowel and a silent part. (4) Other phonetic segments; devoiced vowels, VCV (V: vowel, C: consonant), etc. As an example, 191 types of phonetic segments selected for a set of utterances of 100 words are indicated below. Continuant segments: Consonant segments: Boundary segments: Boundary parts between vowels and semi-vowels Boundary parts between vowels and consonants Boundaries between vowels and silent parts Other segments: Devoiced vowels and others AA1, AA2 in the continuant segments indicate that the latter has been cut out from a vowel [a] with a light stress. II3, UU3 are unvoiced segments. NN1 through N5 correspond to different phonemic environments. BZ1 through ZZ1 correspond to vocal cord sounds and other phenomena. HHA through HHO correspond to unvoiced [h], and HVA through HVE correspond to voiced [h]. Next, QA1 in the consonant segments indicates a vowel at the onset of a word, and KA1, KA2 indicates that the latter has, in principle, been cut out from within a word. For example, [t ∫ a], which is one of contracted sounds, is constituted with a boundary segment interposed as CA1 → YA1 → AA1 (with actual speech, the transition can occur from CA1 to AA1 or AA2). As boundary segments, the boundary parts between vowels and semi-vowels (AI1), the boundary parts between vowels and consonants (AS1), and the boundary parts between vowels and silent parts (AQ1) are entered. Note that, in the segments representing the boundary parts between vowels and semi-vowels, the syllabic nasal is denoted by NN (ANN). The other segments include VCV segments (ANA) in which consonants are about to be devoiced, and segments in which vowels are about to be devoiced, which are observed when the speed of utterance is high. In PS dictionary 13b stored is such information on 191 types of speech segments as an orthogonalization dictionary. The operation of the sound analyzer and the matrix quantizer is described in detail in the specification of U.S. Patent No. 4,888,823 assigned to the same assignee as this application. When such PS is used as a segment at the time of quantization, problems are the number of dimensions of feature parameters of a segment (herein the orders of LPC mel-cepstrum) and the time width (the number of frames). That is, for the steady part of a vowel, it is required that the number of dimensions of its feature parameters be large, but the number of its frames may be small. Moreover, a plosive consonant must to some extent be large in the number of dimensions of its feature parameters and in the number of its frames. Furthermore, a fricative consonant may be small in the number of dimensions of its feature parameters but needs a large number of frames. Thus, in the present invention, the feature parameters and the number of frames of each PS are set as follows: (feature parameters, the number of frames); A = (16, 4) B = (14, 6) C = (12, 8) D = (10, 10) E = ( 8, 12) A selection is made from among these combinations. Of the four PS-type indicating characters described above, the last character indicates this setting. As a result, a vowel such as AA1A can be set so that the number of dimensions of its feature parameters is so large as to be 16, and a fricative consonant such as ZE1E can be set so that the number of its frames is as large as 12. Thereby, the total number of dimensions of the entire PS is brought within a relatively narrow range of 64 to 100. This enables a statistical matching process (for example, subspace method) to reduce quantization distortion. To see the accuracy of a PS similarity sequence obtained by matrix quantizer 13a, an evaluation experiwas made on a spoken word by obtaining a PS sequence of the first rank and inputting its symbol sequence to the discrete HMM. The experiment was performed on word utterances of unspecified speakers. As a result of this experiment, a recognition rate of 98.4% was obtained for 32 words, which is substantially the same as that by a conventional pattern matching method performed on a word-by-word basis. However, for 32 words comprised of pairs of similar words, only 91.0% was obtained. The experiment has shown that, with the discrete HMM employing a symbol sequence of the first rank, the quantization error still remain great, and the HMM needs to be treated as a continuous distribution. However, the direct application of the continuous distribution HMM to a vector S = (S1, S2, ..., Sn) composed of n kinds of PS similarities is inexpedient because huge quantities of calculations are required. Thus, a method which applies the continuous distribution HMM was adopted after a similarity space Rn of phonetic segments has efficiently been integrated into a phonemic feature space Rm (m << n). Many of phonetic segments PS are designed to represent phonemes under various environments appearing in an utterance as described above. For this reason, a correspondence between phonetic segments and phonemes is relatively easy to establish. For example, phonetic segments corresponding to the /r/ phoneme includes the following 44 types: Here, of numerals attached to the segment names, "1" indicates that the segment is at the head of a word, and "2" indicates that the segment is within a word. Like ARA, the segments that are sandwiched by vowels are of the VCV type. There are various methods of integrating phonetic segments into a phoneme. A general form is represented by equation (1) S phoneme = f{S PS(i)} where f { } indicates a function for integrating similarity values S ps(i) (or distance values) of similarity values S ps(i) of phonetic segments PS(i) that belong to a phoneme (for example, /r/). As an example of f { }, there is the following maximum-value filter. f = max{S ps(i)} Equation (2) indicates the maximum value of the similarity values S ps(i) of phonetic segments belonging to a phoneme (for example, /f/) is taken as the score S phoneme of the phoneme. Fig. 4 illustrates the case where maximum-value filters are provided in parallel to integrate a PS similarity vector into a phonetic score vector small in the number of dimensions. Next, Fig. 5 illustrates an example of integration using neural networks. In Fig. 5, a three-layer neural net is provided for every m phonemes, and similarity values Sps (i) of a phonetic segment PS(i) are passed through the neural network for each phoneme, whereby they are integrated as the phonetic score S phoneme. The arrows in Fig. 5 indicate weighting coefficients of the neural nets. The weighting coefficients can be obtained by a well-known back propagation algorithm using learning data. The provision of independent neural networks in parallel permits PS similarity vectors to be integrated into phonemic score vectors small in the number of dimensions. Generally used phonemes include 19 types of phonemes of {o, a, e, i, u, h, j, w, r, n, m, z, s, g, k, d, t, b, p}. In the present embodiment, in addition to the 19 types of phonemes, a Japanese independent nasal sound |N|, a vowel at onset of a word, a continuant consonant C, a transient part from a vowel to a consonant, and a transient part X from a vowel to a silent part are used as phonemic features. That is, the present embodiment uses 24 types of phonemic features. Thereby, the PS similarity sequence is transformed into a sequence of 24-dimensional phonemic feature vectors by PS-phoneme integrating section 14 which carries out the process indicated by equation (1) and then sent to a HMM recognizer 15. Fig. 2 illustrates an example of a phonemic feature vector corresponding to a word utterance "KENGO" (robust). Next, the word matching of the present invention using HMM will be described. It is supposed that the HMM has N states, S1, S2, ..., Sn, and the initial state is distributed to the N states in a statistical manner. In speech, a model is used which makes a state transition with a certain probability (transition probability) with each frame cycle. At the occurrence of a transition, a label is output with a certain probability (output probability). However, a null transition that outputs no label might be introduced. Even if an output label sequence is given, a state transition sequence is not determined uniquely because there are a number of ways in which the state transition is made. Since it is only a label sequence that can be observed, the model is called the hidden markov model. The HMM model M is defined by the following six parameters. N: the number of states (the states S1, S2 ..., SN, N=10 in experiment) K: the number of labels (label L = 1, 2, ..., K, K= 191 in experiment) pij: the transition probability (the probability of transition from Si to Sj) qij(k): the probability of outputting of the label k at the transition from Si to Sj mi: the initial state probability (in the experiment, the initial state is restricted to S1) F: the set of final states (in the experiment, the final state is restricted to S10) Next, transition restrictions reflecting speech features are made on the model. In speech recognition, such a loop as makes return from a state Si to a previous state (Si-1, Si-2, ...) is generally not allowed since time sequence is disturbed. Fig. 3 illustrates a typical example of the HMM structure described above. The learning of the HMM needs to apply a label sequence O to HMM learning section 16 and to estimate the parameters of model M in which Pr (O/M) is maximum. As an algorithm used for the estimation, the forward-backward algorithm is known. The evaluation (recognition) of the HMM needs to obtain the probability Pr(O/M) that the model M will output the label sequence O = O1, O2, ..., OT. That is, HMM recognizer 15 assumes models for each word and retrieves such a model (word) as Pr(O/M) is maximum using the Viterbi algorithm. The above description depends mainly on the discrete HMM. With the continuous output probability distribution HMM (hereinafter referred to as the continuous HMM), the input is not a label sequence but a vector (in the present invention, a phonemic feature vector). This permits the provision of the vector occurrence distribution in place of the above output probability qij(k) (the probability of outputting of the label k at the time the transition is made from Si to Sj). Usually, the distribution (the distribution of the elements of a vector) is treated as a normal distribution or a mixture of normal distributions. The continuous HMM model is defined by the six following parameters. N: the number of states (state S1, S2, ..., SN; in the experiment N = 10) pij: the transition probability (the probability of transition from Si to Sj) µij: the mean vector occurring at the time of the transition from Si to Sj Σij: the covariance of a vector occurring at the time of the transition from Si to Sj mi: the initial state probability (in the experiment, the initial state is restricted to S1) F: the set of final states (in the experiment, the final state is restricted to S10) In the mixed distribution, more than one mean vector and more than one vector covariance are given. The learning and evaluation (recognition) of the continuous HMM are performed by using the forward-backward algorithm and the Viterbi algorithm, respectively, as in the discrete HMM. In the experiment, many pieces of learning data (phonemic feature vectors) were applied to HMM learning section 16, and the parameters of each model M corresponding to 32 words were estimated so as to maximize the probability Pr(O/M) (with the continuous HMM, O is a vector sequence). The model parameters (µij and Σij for each word) thus obtained are stored in HMM word model buffer 17. Next, at the stage of recognition, HMM recognizer 15, for each model stored in HMM word model buffer 17, obtains the probability Pr(O/M) of phonemic feature vector for input speech and finds such a model as the probability is maximum. The word corresponding to the model is the result of recognition. The evaluation experiment of the present invention was conducted on the same data (32 similar words) as in the above-mentioned experiment using the discrete HMM. The experiment was made for the case using a single normal distribution as the vector occurrence distribution and for the case using two normal distributions (mixed distribution). However, the covariance uses only diagonal elements because of an insufficient amount of learning data. The results were 91.3% in the case of the single distribution and 92.4% in the case of the mixed distribution, which exceed the result (91.4%) of the experiment using the discrete HMM. The experiment has shown that the present invention is excellent. According to the embodiment, as described above, with a phonetically significant phonemic segment (PS) used as a unit, a statistical matrix quantization process is performed, and a recognition process is carried out using HMM after a PS similarity sequence has been integrated into a phonemic feature vector, permitting various distortions occurring in continuous speech to be absorbed effectively, thereby realizing high-accuracy speech recognition. In addition, the embodiment has a great advantage in that there is no need for huge quantities of calculations. Although, in the above embodiment, matrix quantization processing is performed on feature vectors obtained by sound analysis, usual vector quantization may be used instead. Moreover, there would be possible such a modification as to pass feature parameters through a recognizer such as a neural network set in units of a phonemic segment, to integrate the output of the recognizer into a phonemic feature vector, and then to pass the phonemic feature vector through continuous output probability distribution HMM. Furthermore, although, in the above embodiment, maximum-value filters are used for integration into a phonemic feature vector, entry of a phonemic segment similarity sequence into a neutral network for integration into a phonemic feature vector would also be possible.

Claims: Speech recognition system comprising: sound analyzing means (12) for sound analyzing an input speech signal to obtain a set of feature parameters for each frame of said speech signal; a phonetic segment dictionary (13b) for storing a plurality of types of phonetic segments in advance, each phonetic segment being identified by a set of feature parameters and a number of frames; and means (13a) for obtaining a sequence of phonetic segment similarity values from said sets of feature parameters obtained by said sound analyzing means and from said phonetic segment dictionary; characterized by integrating means (14) for integrating said sequence of phonetic segment similarity values obtained by said phonetic segment similarity sequence obtaining means (13a) into a sequence of phonemic feature vectors; and means (15, 17) for checking said sequence of phonemic feature vectors obtained by said integrating means using a hidden markov model (HMM) prepared in certain units, thereby performing a recognition process. System according to claim 1, characterized in that said means (13a) for obtaining a sequence of phonetic segment similarity values comprises matrix quantization means for performing matrix quantization processing between said sets of feature parameters obtained by said sound analyzing means and said phonetic segment dictionary. System according to claim 1, characterized in that said means (13a) for obtaining a sequence of phonetic segment similarity values comprises vector quantization means for performing vector quantization processing between said sets of feature parameters obtained by said sound analyzing means and said phonetic segment dictionary. System according to claim 1, characterized in that said means for obtaining a sequence of phonetic segment similarity values comprises a neural network for obtaining the sequence of phonetic segment similarity values from said sets of feature parameters obtained by said sound analyzing means. System according to claim 1, characterized in that said integrating means comprises maximum-value filters for integrating said sequence of phonetic segment similarity values into a sequence of phonemic feature vectors. System according to claim 1, characterized in that said integrating means comprises neural networks for integrating said sequence of phonetic segment similarity values into a sequence of phonemic feature vectors. Speech recognition method comprising the steps of: a) analyzing an input speech signal to obtain a set of feature parameters for each frame of said speech signal; and b) obtaining a sequence of phonetic segment similarity values from said sets of feature parameters obtained by said step a) and from a phonetic segment dictionary (13b) for storing a plurality of types of phonetic segments in advance, each phonetic segment being identified by a set of feature parameters and a number of frames; characterized by c) integrating said sequence of phonetic segment similarity values obtained by said step b) into a sequence of phonemic feature vectors; and d) checking said sequence of phonemic feature vectors obtained by said step c) using a hidden markov model (HMM) prepared in certain units, thereby performing a recognition process. Method according to claim 7, characterized in that said step b) comprises a matrix quantization step for performing matrix quantization processing between said sets of feature parameters obtained by said sound analyzing step a) and said phonetic segment dictionary. Method according to claim 7, characterized in that said step b) comprises a vector quantization step for performing vector quantization processing between said sets of feature parameters obtained by said sound analyzing step a) and said phonetic segment dictionary. Method according to claim 7, characterized in that said step b) uses a neural network to obtain the sequence of phonetic segment similarity values from said sets of feature parameters obtained by said sound analyzing step a). Method according to claim 7, characterized in that said step c) uses maximum-value filters to integrate said sequence of phonetic segment similarity values into a sequence of phonemic feature vectors. Method according to claim 7, characterized in that said step c) uses neural networks to integrate said sequence of phonetic segment similarity values into a sequence of phonemic feature vectors.

Inventors: NITTA TSUNEO, NITTA, TSUNEO, Nitta, Tsuneo, c/o Intellectual Property Div.
Applicants: TOSHIBA KK, KABUSHIKI KAISHA TOSHIBA
Application year: 1992
Main classification: G10L5
IPC codes: G09B19, G10L11, G10L15