METHOD AND APPARATUS FOR SPEECH RECOGNITION ADAPTED TO AN INDIVIDUAL SPEAKER

Abstract: A method and apparatus for automatic recognition of speech adapts to a particular speaker by using adaptation data to develop a transformation through which speaker independent models are transformed into speaker adapted models. The speaker adapted models are then used for speaker recognition and achieve better recognition accuracy than non-adapted models. In a further embodiment, the transformation-based adaptation technique is combined with a known Bayesian adaptation technique.

Description: METHOD AND APPARATUS FOR SPEECH RECOGNITION ADAPTED TO AN INDIVIDUAL SPEAKER COPYRIGHT NOTICE A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. BACKGROUND OF THE INVENTION This invention was supported in part by grants from the Office of Naval Research, Contracts N00014-93-C-0142 and N00014-92-C-0154, and in part by SRI International of Menlo Park, California. The Government has certain rights in this material. This invention relates to automatic speech recognition and, more particularly, to a method and system for adapting the models used in a speech recognition system to a particular speaker. This art presumes a basic familiarity with statistics and Markov processes, as well as familiarity with the state of the art in speech recognition systems using Hidden Markov Models. The state of the art was discussed at length in related U. S. patent application serial number 08/276,742 filed July 18, 1994 and that discussion is incorporated herein by reference including the discussion of all prior art references cited. By way of example of the state of the art in the particular field of adapting speech recognition systems to particular speakers, reference is made to the following patents and publications, which have come to the attention of the inventors in connection with the present invention. Not all of these references may be deemed to be relevant prior art. Inventor U.S. Patent No. Issue Date Bahl et al. 4,817,156 03/28/89 Kuroda et al. 4,829,577 05/09/89 Papers L. R. Bahl, F. Jelinek and R. L. Mercer, "A Maximum Likelihood Approach to Continuous Speech Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. PAMI-5(2), pp. 179-190, March 1983. J. Bellegarda, "Robust Speaker Adaptation Using a Piecewise Linear Acoustic Mapping," Proceedings ICASSP, pp. I-445-I-448, San Francisco, CA, 1992. P. Brown, C.-H. Lee and J. Spohrer, "Bayesian Adaptation in Speech Recognition," Proceedings ICASSP, pp. 761-764, Boston, MA, 1983. K. Choukri, G. Chollet and Y. Grenier, "Spectral Transformations through Canonical Correlation Analysis for Speaker Adaptation in ASR, "Proceedings ICASSP, " pp. 2659-2662, Tokyo, Japan, 1986. S. Furui,"Unsupervised Speaker Adaptation Method Based on Hierarchical Speaker Clustering," Proceedings ICASSP, pp. 286-289, Glasgow, Scotland, 1989. X. Huang and K.-F. Lee, "On Speaker-Independent, Speaker-Dependent and Speaker-Adaptive Speech Recognition," IEEE Trans, on Speech and Audio Processing, Vol. 1, No. 2, pp. 150-157, April 1993. B.-H. Juang, "Maximum-Likelihood Estimation for Mixture Multivariate Stochastic Observations of Markov Chains," ATλ&T Technical Journal, Vol.64, No.6, July-August 1985. C.-H. Lee, C.-H. Lin and B.-H. Juang, "A Study on Speaker Adaptation of the Parameters of Continuous Density Hidden Markov Models," IEEE Trans, on Acoust., Speech and Signal Proc., Vol. ASSP-39(4), pp. 806-814, April 1991. R. Schwartz, Y. L. Chow and F. Kubala, "Rapid Speaker Adaptation Using a Probabilistic Spectral Mapping," Proceedings ICASSP, pp. 633-636, Dallas, TX, 1987. A recent trend in automatic speech recognition systems is the use of continuous-mixture-density Hidden Markov Models (HMMs). A system and method for using HMMs to recognize speech is disclosed in related U.S. patent application serial number 08/276,742 assigned to the assignee of this application. Despite the good recognition performance that HMM systems achieve on average in large vocabulary applications, there is a large variability in performance across individual speakers. Performance can degrade rapidly when the user is radically different from the training population, such as a user who speaks with a heavy accent. One technique that can improve the performance and robustness of a speech recognition system is to adapt the system to the speaker, and, more generally, to the channel and the task. Two families of adaptation schemes have been proposed in the prior art. One is based on transforming an individual speaker's feature space so that it "matches" the feature space of the training population. This technique may be generally referred to as the Feature-Space Transformation-based approach (FST). This technique has the advantage of simplicity, and if the number of free parameters in the transformations is small, then this technique has the desirable characteristic of quick adaptation. The second main family of adaptation methods follows a Bavesian approach of progressively transforming the HMMs so that the models best predict adaptation data from the individual speaker. In a Bayesian approach, model parameters are re-estimated using some prior knowledge of model parameter values. The Bayesian approach usually has desirable asymptotic properties, that is, the performance of the speaker-adaptive system will converge to the performance of a speaker-dependent trained system as the amount of adaptation speech increases. This method has the disadvantage that the adaptation rate is usually slow. What is needed is a speaker adaptive method and system that has superior performance for individual speakers, including those who speak with very different accents from the traimng population, but that can adapt quickly to a particular speaker using a small amount of adaptation data. SUMMARY OF THE INVENTION According to the present invention, a speech recognition system adapts itself to a particular speaker by applying a constrained maximum likelihood transformation (CML) to its stored speech recognition models. This CML approach represents a third main family of adaptation method. The constrained linear transformation is derived from a relatively small amount of speaker dependent (SD) training data. Applying the transformation to the speaker independent (SI) models stored in the recognition system produces a set of speaker adapted (SA) models. When these SA models are used by the recognition system according to the present invention, greatly improved recognition performance of an individual speaker is achieved. The recognition performance improvement over a non-adaptive system is even greater when the speaker speaks very differently from the training population, such as a speaker with a heavy accent. According to a further embodiment, a speech recognition system uses the CML approach in combination with a Bayesian approach to achieve superior performance. Figs. 1A, IB, IC are a representation of the known Bayesian Approach, the CML Approach of the present invention, and the Combination Approach. Fig. 1A depicts the CML approach according to the invention. Adaptation data 12 is used to derive a transformation 22. This transformation 22 is then used to transform the parameters of the SI Models 10 to derive SA Models 20. Fig. IB depicts the known Bayesian approach for adapting a speech recognition to a particular speaker. The system starts initially having some "prior knowledge" about model parameters, represented as the SI Models 10. Adaptation data 12 from a particular speaker is then processed by a Baum-Welch algorithm 14 to produce SD Models 16. According to d e Bayesian approach, these SD Models are then combined with SI models by adder to produce a set of SA recognition models 20. According to the Bayesian approach, the SA models 20 can then be substituted in the method for the prior knowledge SI models 10 and the process can be repeated to better improve the SA models. Fig. IC depicts the Combination approach according to the invention. SA Models 20 are derived as in the CML approach and are then used as "prior knowledge" in a Bayesian type approach to produce SA" Models 22 by combining the SA models with SD models 16. One particular type of speech recognition system in which the present invention may be effectively employed uses multi-dimensional Gaussian mixture densities to model the probability functions of various speech states that are stored in the recognition models. The Gaussian mixture densities are derived from the SI training data when the models are trained and are stored in the recognition system as a weighted sum of simple Gaussian curves. The SI training data typically consist of speech samples from a number of different speakers (the "training population") each speaking a transcribed speech sample. The Gaussian mixture density of a particular state in such a system is generally stored as a weighted sum of simple Gaussian curves. The set of simple Gaussian curves used to model a particular state is referred to in the art as a "codebook. " In a fully-tied speech recognition system, one codebook of simple Gaussian curves is used to model the probability density functions of all of the speech states in the system and each probability density function has associated with it only a set of weighting coefficients that are applied to the Gaussians in the shared codebook. In a fully-untied system, a separate codebook of simple Gaussian curves is used to model the probability density function of each separate speech state in the system. Related patent application SN 08/276,742 disclosed a type of a partially- tied system, in which there are multiple codebooks, and a single codebook of simple Gaussian curves is used to model the probability density function of a group or "cluster" of related speech states. According to one specific embodiment, the invention achieves rapid and efficient adaptation in such a recognition system by assuming that the same transformation can be effectively used for all of the Gaussians in a particular codebook. Therefore, with even a small amount of SD data, a transformation can be computed for all of the stored Gaussians. According to a further embodiment of the invention, a linear transformation may be shared by different related codebooks. The invention, according to one embodiment, uses a CML approach to estimate parameters for transforming the speech recognition models, rather than transforming the speech data directly. This technique allows the invention to use the well-known Expectation-Maximization (EM) algorithm to determine Maximum Likelihood (ML) estimates of these parameters. The advantage of using the EM algorithm is that the invention can estimate the transformation from new-speaker SD data alone without the need present in some prior art systems for some form of time alignment between the new-speaker data and the traimng data. Furthermore, in contrast to previous adaptation schemes based on feature transformations, the present invention has the advantage that it does not require the new speaker to record sentences with previously specified transcriptions. The invention was evaluated on the large-vocabulary Wall Street Journal corpus, for both native and non-native speakers of American English. For native speakers, the recognition performance after adaptation is similar to that of speaker-dependent systems that use 6 times as much training data. For non-native speakers, d e recognition error rate is approximately halved, approaching that of native speakers, with only a small amount of adaptation data. BRIEF DESCRIPTION OF THE DRAWINGS Figures 1A-C are diagrams illustrating adaptation using model transformation according to the invention, adaptation using Bayesian techniques, and adaptation using combined model transformation and Bayesian techniques according to the invention; Figure 2 is a diagram of an HMM for a single phone; Figure 3 is a diagram illustrating a method for fitting a mixture of Gaussian curves to the observed feature data of one state of a phone; Figure 4 is a block diagram of a speech recognition system built according to one embodiment of the invention; Figure 5 is a block diagram of a speech recognition system built according to a further embodiment of the invention; Figure 6 is a graph showing SI, SA, and SD word error rates for native speakers; Figure 7 is a table showing SI and SA word error rates for non-native speakers for various degrees of mixture tying and various numbers of transformations; Figure 8 is a graph showing word error rates for various numbers of transformations for transformation based adaptation; and Figure 9 is a graph showing word error rates for various numbers of transformations for combined adaptation; Figure 10 is a graph comparing error rates for transformation-only, approximate Bayesian, and combined methods; and Figure 11 is a table showing adaptation results using bigram and trigram language models. DESCRIPTION OF PREFERRED EMBODIMENTS For purposes of clarity, the method and system of the invention will be described with reference to a particular embodiment of the invention as incoφorated in a speech recogmtion system using HMMs with Gaussian mixtures and having an arbitrary degree of mixture-tying as disclosed in previously referenced patent application SN 08/276,742. However, it will be obvious to one of skill in the art that the method of the invention may be employed in a wider range of recognition systems, particularly any system using continuous mixture density HMMs. A common technique for matching an input speech signal to stored phones uses Hidden Markov Models (HMMs). One HMM speech recogmtion system constructs, for each phone, a three-state directed HMM. Training the HMM consists of extracting features from a collection of speech samples from various speakers speaking known phrases of speech and using the extracted features to determine optimal HMM parameters. The process of extracting features divides a speech signal into a number of temporal windows, generally of equal duration, referred to in the art as frames. Once the parameters for each HMM have been determined, the trained HMMs, with their parameters, are stored in the speech recognition system. The recognition system is then ready to process an unknown speech signal and determine the most likely words spoken by comparing features extracted from that speech with the probability functions for speech states stored in the HMMs. Fig. 2 depicts one type of three-state HMM for a single phone, such as the phone "ao" representing die first vowel sound in the word "water." The three states, s„ & 2 , and S 3 , are associated with a collection of features that might be detected during a given frame. Associated with each transition between states is a probability of taking that transition between frames. This probability is not set at the time the model is selected, but it is determined in the training step using actual speech data. The transition probability distributions implicitly model varying durations possible in different parts of speech due to different speech rates or different pronunciations. Associated with each of the three possible HMM states are probability functions P(y| s,), Pfylsj), and P(y|s 3 > that give the probability that an observed feature set y was generated by a speech signal in that respective state. As an example, the function P(y| s,) provides the probability of observing y at state s, rather that a different feature set y\ The HMM further includes six transitions tl-6, with associated probability functions P(s, |s,), P(Sj|s,), P(s 2 | s 2 ), PfelSa), P^ ), P(s βxfc |s 3 ). According to the model, a transition occurs at every frame boundary and the aforementioned transition probability functions determine the probability that a particular transition will be taken given that the system is in the particular state. As part of the task of selecting a model for a speech recognition system, the system designer determines the form of the probability equations P(y| s,), P(y |Sj), P(y j S 3 ). One form for these equations that has been effectively utilized is to represent the equations as a sum of weighted Gaussian curves. Such a sum is represented in Fig. 2. Curve 60 represents P(y|s.) as determined from the observed training feature data. The speech recognition system designer wishes to develop a probability function for P(y| s,). This probability function must "fit" the observed data 60. It has been found in existing speech recognition systems that the training data may be represented by a sum of weighted Gaussian probability functions, shown as curves 62a-e. (These curves 62a-e are a one-dimensional representation of the actual multi-dimensional Gaussian functions.) This weighted Gaussian sum is referred to in the art as a "Gaussian mixture. " If each Gaussian curve, 62a-e, is represented by a weighted Gaussian function λjGj(y), then the probability P(y|s,) may be represented by the function: where s represents the HMM state, y the observed features at a given frame t, Gj(y) a series of Gaussian curves, and λ, a series of weighting constants. If the speech recognition system designer selects a model using a probability function in form of equation (1), the task of training the model is to determine a set of Gaussian curves Q and weighting factors λ, that best fit the observed training data for each HMM state. Once the Gaussian curves and weighting factors are determined, they are stored by the system for use in recognition. A speech recognition system according to the present invention adapts the stored Gaussian curves G, and in one embodiment, the weighting constants λ, to a particular speaker in order to improve recognition performance of that speaker. One characteristic that distinguishes prior art recognizers is the extent to which the stored Gaussian curves 62a-e are shared between different HMM states. In a fully continuous HMM system using Gaussian mixtures, there is no sharing. A different set of Gaussian curves G si is estimated and stored for each different HMM state. Other systems have some degree of mixture tying. In such a system, the same set of Gaussian curves is used for more than one HMM state and only the weighting coefficients, λ,,are modified for HMM states that are tied. The term "codebook" is used generally in the art to denote the set G α of Gaussian curves used to approximate the HMM states in a tied- mixture system. Fig. 4 is a block diagram of a speech recognition system built according to the invention. The system consists of a microphone 100, a feature extraction module 103, a training engine 107, a recognition engine 110, an adaptation engine 113, a set of stored SI models 117, a clustering engine 120, a set of stored SA models 123, and an output device 125. Operation of the system occurs as follows. Microphone 100 receives speech signals. Extractor 103 decodes the signals into data that can be manipulated by the rest of the system. Initially, the speech signals are training data consisting of transcribed sentences spoken by a group of speakers. Training engine 107 uses these transcribed sentences to determine initial parameters for untied codebooks for the speech states in the system and stores those parameters in 117. Clustering engine 120 operates on the trained models 117 to create a set of tied models according to a method disclosed in above referenced related patent application. Clustering engine 120 identifies states sharing similar Gaussians, groups those states into clusters, splits tiie clusters, and then creates an individual "codebook" or "genone" for each cluster. The codebooks contain a set of Gaussians used to estimate the states in that cluster. After operation of clustering engine 120, the probability function stored in 117 for a state s may be written as P(V t ' S t ) ' ^ λiG a (y t ) (2) where G α is a Gaussian in the codebook for cluster C. Gaussian codebooks created by clustering engine 117 with an arbitrary degree of mixture tying may be referred to as "genones" and HMM systems with an arbitrary degree of genone sharing are referred to as genonic HMMs. Once clustering engine 117 has converted the SI models to genonic SI HMMs, die models may be used by recognition engine 110 to recognize untranscribed speech as follows. Untranscribed speech is received by microphone 100 and its features are extracted by 103. Extractor 103 supplies feature data set x to recognition engine 110. Recognition engine 110 uses the models stored in 117 having the form of (2) to determine a most probable path of speech states for data x. This most probable state path is associated with particular words as depicted in Fig. 1. Recognizer 110 may display the recognized words on output device 125. Once the recogmtion system contains a fully trained set of SI models of the form of equation (2), recognition engine UO is ready to recognize untranscribed speech. However, a new speaker using the system may have an accent or speech pattern that is very different from the traimng data used to train the models. In such a case, while a majority of words and states may be recognized by recognition engine 110 using SI HMMs 117, the recognition error rate will be high. Adaptation engine 113 may be used according to the present invention to improve the performance of recogmtion engine UO as follows. Signals from a new speaker speaking into microphone 100 are processed by extractor 103 and the feature data set y is supplied to recognition engine 110 and to adaptation engine 113. Recognition engine 110 then determines recognized states from the feature data, and makes those states known to adaptation engine 113. Adaptation engine 113 then compares the stored SI data for e recognized states with the actual data from the new speaker and determines a set of transformations for the probability models for those states so that the probability models better fit the actual data from the new speaker. After receiving a limited number amount data from the new speaker, adaptation engine 113 then applies the transformations to all the states in the SI HMMS to generate a set of SA HMMs 123. Once SA HMMs 123 are generated, those HMMs are used by recognition engine 110 to identify states in the incoming speech signal. According to one embodiment, adaptation engine 113 may continue to improve the SA HMMs throughout the time that the new speaker is speaking such that the SA HMMs gradually approach die form of models developed for that particular speaker. The transformation employed by adaptation engine 113 of die present invention may be more easily explained if the probability density function of equation (2) is rewritten as a speaker-independent HMM for the SI vector process [yj witii observation densities of the form: Psι< t I *) = ∑ P(« s f )N(y t ;μ le ∑Jι (3) where P(w,|S ι ) is a coefficient matrix corresponding to λ, , and N(y,; / /.,;∑j) is a Gaussian function with a mean μ, and a covariance Σ,, N w is an integer equal to the number of Gaussians in die particular codebook, and g is die Gaussian codebook (or genone) index. Adaptation of mis system is achieved according to one embodiment of me invention by jointly transforming all me Gaussians of each genone. Specifically, the invention assumes diat, given the genone index of die HMM state s,, me speaker-dependent vector process [x can be obtained by die underlying speaker-independent vector process [yj through die transformation x, = Ay, + b g . This model is equivalent to assuming that me random vector x is obtained through an affine transformation x = Ay + b from die unobserved vector y tiiat has a known mixture density AT. 8iy) = ∑P(ω N(y,μ p Σ). «> i-l Maximum Likelihood (ML) estimation of die constrained Gaussian-mixture model is equivalent to estimating the regression parameters A, b using only observations of the dependent variable x and die knowledge of the distribution of die unobserved variable y. Applying die transformation, die SA observation densities according to the invention have die form I s ) ■ Σ P( ω . I * N ( x t- + b ? Afii t) (5) and only the transformation parameters A g , b v g — 1 ,..., N g need to be estimated by engine 113 during adaptation, where N g is die number of distinct transformations. The same transformations can be applied to different HMM states, and this tying of transformations can be used to optimize performance based on me amount of available adaptation data. Adaptation engine 113 estimates the transformation parameters A g , b v g — 1 ,..., N g according to an extension of die known Expectation Maximization (EM) metiiod for die constrained estimation of Gaussian mixtures. The training procedure according to the invention using the Baum-Welch algoridim is as summarized below: 1. Initialize all transformations with A g (0) = I, b g (0) = 0, g = 1,..., N. Set k = 0. 2. E-step: Perform one iteration of the forward-backward algorithm on die speech data, using Gaussians transformed witii the current value of me transformations A g (k), b g (k). For all component gaussians and all genones g collect die sufficient statistics: r ig = — ∑ Y,(*, ) u (s)x, (7) I ig «.*ι ' — Σ YrWr ) (x rϊig ) ( *ι (8) % = Σ ,(s t )Φ„(s- (9) where y,(sj is die probability of being at state s, at time t given the current HMM parameters λ k , the summation is over all times and HMM states that share die same mixture components, and φ sj is the posterior probability ΦM = p (ω iff I A g {k),b g {k),x tt s) ( 10) 3. M-step: Compute the new transformation parameters. Under die assumption of diagonal covariance and transformation matrices, die elements a and b of A g (k + 1), b g (k + 1) can be obtained by solving die following equations for each g where me offset b is given by (ID where for simplicity we have dropped die dependence on g. The variables μ,, σ 2 ,, μ, ^ are me elements of the vectors and diagonal matrices μ % , ∑j g , μ , Σ^, respectively. 4. If me convergence criterion is not met, go to step 2. Because the Gaussian adaptation transformation employed by me invention is an instance of die Baum-Welch algoridim for HMMs with constrained mixture densities, it can be implemented efficiently. Specifically, die sufficient statistics are me same as in the case of unconstrained mixture densities. Hence, me E-step at each iteration of the adaptation algoridim requires die computation and storage of these statistics and is equivalent to d e E-step of die Baum-Welch algoridim for unconstrained mixture densities. The computational requirements of me M-step are very small compared to the E-step. According to the invention, die above transformation can also be modified to monotonically approach speaker-dependent (SD) training as die amount of adaptation speech is increased by setting a tiireshold and re-estimating without constraints all individual Gaussians for which me number of samples assigned to them is larger than die threshold. Hence, all Gaussians with a sufficiently large amount of adaptation speech are re-estimated independently, whereas Gaussians with little or no adaptation data are adapted in groups. In addition, if die total amount of adaptation data for a particular genone is less than a prespecified threshold, then an identity transformation is used for all of its Gaussians. Once the transformation parameters are determined, die constrained ML estimates for the means and covariances can be obtained using cm. Λ , (13) COMBINING TRANSFORMATION AND BAYESIAN-BASED ADAPTATION According to a further embodiment of the invention, illustrated in Fig. 5, me transformation-based speaker adaptation technique is effectively combined with a known Bayesian adaptation technique to achieve superior recognition performance. In Bayesian adaptation techniques, die limited amount of adaptation data is optimally combined with die prior knowledge derived from the speaker independent training data. Witii me appropriate choice of die prior distributions, die maximum a posteriori (MAP) estimates for die means and covariances of HMMs with single-Gaussian observation densities can be obtained using linear combinations of the speaker-dependent counts and some quantities mat depend on the parameters of the prior distributions (See Lee 1991). The term counts denotes die sufficient statistics collected by performing one iteration of die forward-backward algorithm on the adaptation data. MAP estimates for die parameters of continuous mixture-density HMMs can also be obtained in the same way, (See Lee 1993). For example, the MAP estimate for die mean μ of the ith Gaussian in the HMM mixture density of state s can be obtained using where y, (s) is the probability of being at state s at time t given die current HMM parameters, and φ ύ (s) is the posterior probability of the ith mixture component The quantities τ f (s), m { (s)sae parameters of the joint prior density of me mixture parameters, which was chosen as a product of me Dirichlet and normal-Wishart densities. The parameter τ,(s) is usually estimated empirically and can be used to control me adaptation rate. Similar estimation formulae can be used for die covariances σ of die Gaussians. Based on the similar formulae for die second order statistics, an approximate MAP (AMAP) can be implemented by linearly combining die speaker independent and die speaker-dependent counts for each component density <α J y**" / '*k<xx .(l -λX∞ ^ (17) where the superscripts on the right hand side denote me data over which the following statistics (counts) are collected during one iteration of die forward-backward algoridim ∞ VΣ Y<(* ) Φ* 5 )*r*,; (20) ".T∑ r ΦtfΦ ( 2D t The means and covariances of each Gaussian component density can then be estimated using the combined counts. The weight λ controls die adaptation rate. This method may be referred to as approximate Bayesian adaptation. Similar adaptation schemes have also appeared for discrete HMMs (See Huang 1993), and can be used to adapt die mixture weights in the approximate Bayesian scheme described here. The weight λ controls me adaptation rate. Using die combined counts, we can compute the AMAP estimates of the means and covariances of each Gaussian component density from AMAP w∑r μ * " ^ AM P (22) Similar adaptation schemes have also appeared for discrete HMMs [11], and can be used to adapt die mixture weights in the approximate Bayesian scheme described here. In Bayesian adaptation schemes, only die Gaussians of die speaker-independent models that are most likely to have generated some of the adaptation data will be adapted to me speaker. These Gaussians may represent only a small fraction of die total number in continuous HMMs with a large number of Gaussians. On the other hand, as the amount of adaptation data increases, the speaker-dependent statistics will dominate the speaker-independent priors and Bayesian techniques will approach speaker-dependent performance; We should, therefore, aim for an adaptation scheme that retains the nice properties of Bayesian schemes for large amounts of adaptation data, and has improved performance for small amounts of adaptation data. We can achieve this by using our transformation-based adaptation as a preprocessing step to transform die speaker-independent models so that they better match die new speaker characteristics and improve the prior information in MAP estimation schemes. To combine the transformation and die approximate Bayesian methods, the invention first transforms the speaker-independent counts using the transformation parameters estimated wim me constrained ML (CML) method described above. m -Λ.sxfXi, (23) (24) The transformed counts can men be combined widi die speaker-dependent counts collected using die adaptation data ( xx 1 ) = Xtxx 1 ) ♦ (1 -λ) ( xx^ - » ) and me combined-method models can be estimated from these counts using <xf° u μ * = β CQlf n ig (26) Σ C * OM This procedure is shown schematically in Figure lc. A block diagram of a speech recogmtion system employing mis procedure is shown in Fig. 5. In addition to the components in Fig. 4, mis recognition system includes a set of speaker dependent models 130 and a combining engine 132 for combining the SD models 132 with the SA models 123 to improve the recognition performance of models 123. EXPERIMENTAL RESULTS OF THE CML METHOD We evaluated our adaptation algoridims on die large-vocabulary Wall Street Journal corpus. Experiments were carried out using SRFs DECIPHER™ speech recognition system configured witii a six-feature front end that outputs 12 cepstral coefficients (c, - c, 3 ), cepstral energy (CQ), and dieir first-order and second-order differences. The cepstral features are computed from an FFT filterbank, and subsequent cepstral-mean normalization on a sentence basis is performed. We used genonic hidden Markov models with an arbitrary degree of Gaussian sharing across different HMM states. For fast experimentation, we used the progressive search framework: an initial, speaker-independent recognizer with a bigram language model outputs word lattices for all die utterances in die test set. These word lattices are men rescored using speaker-dependent or speaker-adapted models. We performed two series of experiments, on native and non-native speakers of American English, respectively. All experiments were performed on the 5, 000-word closed-vocabulary task, and are described below. To compare SI, SD and SA recognition performance on native speakers, we performed an initial study of our adaptation algorid ms on die phase-0 WSJ corpus. We used phonetically tied mixture HMM systems, with all allophones of ύ e same context-independent phone sharing die same mixture components, that is, we used systems with one genone per phone. Speaker-independent systems were trained on 3,500 sentences from 42 male speakers. The different cepstral features were modeled as independent observation streams, and each codebook used 50 Gaussians for die vector features and 15 Gaussians for the scalar (energy) features. There was a total of 6,300 phonetic models, each with three states. The number of distinct output distributions was clustered down to 6,300 (a 3-fold reduction) using state-based clustering since a more compact system witii fewer parameters is better suited for adaptation. The performance of the adaptation algoridim was evaluated on 1 0 sentences from each of six male speakers (001, 00b, 00c, OOd, 400, and 431) for varying amounts of training/adaptation sentences. The SI word error rate for tiiese speakers was 15.51 %, including deletions and insertions. We also evaluated the SD performance by separately training a speaker-dependent system for each one of the six speakers using 600 utterances, and found that the SD error rate was 11.51 %. We men tested die adaptation algorithm using a small amount of adaptation data (40 utterances), and the word error rate after adaptation was 13.60%. Thus, with 40 adaptation sentences, 60% of the gap between SI and SD performance was overcome. We then evaluated me SA system performance for varying amounts of adaptation data, using three of me speakers. The results are summarized in Fig. 6. With 100 adaptation sentences, tiie adaptation scheme achieves the performance of a speaker-dependent system tiiat used 6 times as much training data. When all die SD training data are used as adaptation data, die SA system achieves a 50% reduction in error rate over d e SI system and a 25% reduction over me SD system. Speaker adaptation becomes a very important technology for non-native speakers, since die SI error rate is too high for any practical application. In testing die adaptation algoridim on die "spoke 3" task of the phase- 1 Wall Street Journal corpus, we focused on improving recognition performance for non-native speakers of American English using adaptation. Since die phase- 1 corpus was available during mis series of experiments, the SI systems were built using 17,000 training utterances from 140 male speakers. To reduce computing requirements we tuned die algoridim using die five male speakers in the phase- 1 WSJ development data set. The evaluation data set was run only once at the end of die development phase. The data set includes 40 test sentences and 40 phonetically balanced adaptation sentences per speaker. The speakers were selected according to their fluency in English, covering strong to light accents. We first tested four different systems to determine die optimal degree of Gaussian sharing for mis task. All of die systems used 11,932 context-dependent phonetic models, each with three states. Context dependency was modeled only within words, since we have found in preliminary experiments that modeling coarticulation across word boundaries does not improve recognition performance for non-native speakers. The numbers of genones used in tiiese systems were 40 (1 genone per phone), 200, 500, and 950. Each genone consisted of a mixture of 32 Gaussian distributions. The SI and SA performance is shown in Fig. 7. The adaptation was applied sequentially to die Gaussian distributions and die mixture weights. In genonic HMMs, an arbitrary degree of mixture tying across different HMM states can be selected tiirough an agglomerative clustering procedure. If the degree of tying is small, and consequently die number of genones is large (as in die 500- and 950-genone systems in Fig. 7), then a large number of linear transformations may have to be estimated during adaptation. We can overcome this problem by using tying of the transformations across different genones, and die agglomerative clustering scheme used for die genone construction is very suitable for mis. Each node in the tree that is generated during the clustering procedure corresponds to a set of states, witii die leaves of die tree corresponding to single HMM states. The degree of tying used in a particular system can be represented by a cut through the tree. The location of the cut is determined by the stopping criterion of the agglomerative clustering. Thus, if we want to use a smaller number of transformations than the number of genones in me system, we can somewhat relax the stopping criterion (i.e., cluster more aggressively) and determine a second cut, at a higher level tiirough the tree. All nodes of die original cut (i.e., all genones) tiiat fall under die same node of die new cut can share die same transformation. The third column in Fig.7 indicates die number of linear transformations used in re- estimating the Gaussian distributions. In die first two systems we used one transformation per genone. In the remaining two systems with large numbers of genones, we grouped die transformations in order to reduce the number of parameters to be estimated. The SI word error rates for die various systems were similar, ranging from 28.7% to 30.1 %. By using tying of me transformations during adaptation for me 950- and 500-genone systems and reducing the number of transformations from 950 and 500 to 200, the SA error rates were reduced from 17.7% and 16.6% to 15.8% and 15.1 %, respectively. The SA error rate of 15.1 % was die lowest overall for all the systems that we examined, and the average improvement due to the adaptation algoridim for the five speakers was 47%. To evaluate die relative contribution of the two stages of our adaptation scheme, we evaluated the SA error rate for our best system with die mixture-weight adaptation disabled. We found that by adapting the Gaussian codebooks only using me constrained estimation method, the SA word error rate was 15.6%. Hence, for continuous HMMs most of the performance gain during adaptation is achieved by adapting die Gaussian codebooks. EXPERIMENTAL RESULTS OF THE COMBINED METHOD We also evaluated die combined method on die "spoke 3" task of die phase- 1, large- vocabulary Wall Street Journal corpus, trying to improve recognition performance for non-native speakers of American English. The speaker-independent continuous HMM systems that we used as seed models for adaptation were gender dependent, trained on 140 speakers and 17,000 sentences for each gender. Each of die two systems had 12,000 context-dependent phonetic models which shared 500 Gaussian codebooks with 32 Gaussian components per codebook. For fast experimentation, we used die progressive search framework: an initial, speaker-independent recognizer with a bigram language model outputs word lattices for all the utterances in me test set. These word lattices are men rescored using speaker-adapted models. We used die baseline 5,000-word, closed-vocabulary bigram and trigram language models provided by Lincoln Laboratory. The trigram language model was implemented using me N-best rescoring paradigm, by rescoring the list of the N-best sentence hypotiieses generated using the bigram language model. In die first series of experiments we used die bigram language model. We first evaluated die performance of die transformation-based adaptation for various numbers of transformations and amounts of adaptation data. As we can see in Fig. 8, where we have plotted die word error rate as a function of the number of adaptation sentences, multiple transformations outperform very constrained schemes that use 1 or 2 transformations. The performance with 20 and 40 transformations is similar, and is better than the less constrained case of 160 transformations. However, as the amount of adaptation data increases, die 160 transformations take advantage of die additional data and outperform die more constrained schemes. A significant decrease in error rate is obtained with as little as 5 adaptation sentences. When adapting using a single sentence, die performance is similar for different number of transformations, except for die case of two transformations. The reason is that in our implementation a transformation is re- estimated only if the number of observations is larger tiian a threshold, odierwise we use a global transformation estimated from all data. Since most of the transformations are backed off to die global transformation for me case of a single adaptation sentence, die schemes with different numbers of transformations exhibit similar performance. In Fig. 9 we have plotted die word error rates of the combined scheme for die same numbers of transformations and adaptation sentences as in Fig. 8. The systems used to obtain die results of Fig. 8 are used as priors for die subsequent Bayesian estimation step, as explained in Section 3. We can see mat the performance of the combined scheme becomes less sensitive to the number of transformations used, especially with larger numbers of adaptation sentences. This behavior should be expected, since Bayesian schemes will asymptotically converge to speaker dependent performance as me amount of adaptation data increases. However, when the number of adaptation sentences is small, it is important to select the appropriate number of transformations and provide die Bayesian step with good prior information. In Fig. 10 we compare the word error rates of the transformation-only method witii 20 and 160 transformations, die approximate Bayesian method with conventional priors, and die combined method for various amounts of adaptation data. In die latter, die number of transformations was optimized according to the available amount of adaptation data. The transformation-only method with 20 transformations outperforms the Bayesian scheme with conventional priors when less tiian 10 sentences are used for adaptation, whereas the situation reverses as more adaptation sentences are used. This is consistent with our claim mat transformation-based methods adapt faster, whereas Bayesian schemes have better asymptotic properties. The performance of the transformation approach for large amounts of adaptation data can be improved by increasing the number of transformations. In die same figure, we can also see me success of the combined method, which outperforms significantly die first two methods over die whole range of adaptation sentences that we examined. The transformation step provides quick adaptation when few adaptation sentences are used, and the Bayesian re- estimation step improves the asymptotic performance. Finally, we evaluated the word error rate of our best performing configuration for 40 adaptation sentences on the development and die previously unseen November 93 ARPA evaluation sets of die WSJ corpus using a trigram language model. Our results, presented in Fig. 11, represent die best reported results to date on this task. The non-native recognition performance after adaptation using only 40 sentences is slightly higher than that of native speakers, which for the same speaker independent models is 9.7% and 7.2% word error witii a bigram and a trigram language model respectively. The first listed inventor, a non-native speaker of American English with a particularly heavy accent, was part of die development set. Since additional adaptation data were available for him, we evaluated die adaptation performance for up to 285 adaptation sentences (approximately 40 minutes of speech). For mis particular speaker, the speaker-independent error rate decreases by a factor of 4 and 6 using 40 and 285 adaptation sentences, respectively. The speaker-adapted error rate of 7.1 % is comparable to die state-of-die-art performance for native speakers on this task. The invention has now been described with reference to specific embodiments. Other embodiments will be apparent to those of ordinary skill in die art. It is dierefore not intended that this invention be limited, except as indicated in die appended claims.

Claims: WHAT IS CLAIMED IS: 1. A speaker adaptive speech recognition system comprising: means for receiving a speech signal from a speaker; feature extraction means for converting said speech signal into a data set of feature vectors; means for storing a plurality of speaker independent models, said models initially having undetermined parameters; a training engine for determining the parameters of said speaker independent models from a set of training data; an adaptation engine capable of receiving speech data from a particular speaker for transforming the parameters of said speaker independent models using maximum likelihood techniques and generating a set of speaker adapted models; and a recognition engine capable of using said speaker independent models and said speaker adapted models to recognize words from a set of unidentified feature vectors. 2. The system according to claim 1 wherein the speaker independent and speaker adapted models are each associated with a subunit of speech and represent mat subunit of speech as a plurality of states, each state having associated with it a probability function, the probability functions having parameters determined from training data, die probability function producing a probability that a given set of speech data is representative of mat particular state. 3. The system according to claim 2 wherein die probability functions are stored in die system as a mixture of simple probability functions. 4. The system according to claim 3 wherein the adapting engine means adapts the system to a particular speaker by transforming the simple probability functions. 5. The system according to claim 1 wherein the simple probability functions are Gaussians. 6. The system according to claim 1 wherein said adaptation engine is capable of transforming said speech models using nontranscribed speech data from said particular speaker. 7. A speaker adaptive speech recognition system comprising: a set of models representing selected subunits of speech, each model having associated witii it a plurality of states and each state having associated with it a probability function, die probability functions having parameters determined from training data said training data derived from a plurality of speakers from a training population; means for collecting a set of speaker adaptation data from a particular speaker, said set of adaptation data comprising words of die speaker's choice spoken by die speaker, said adaptation data not necessarily comprising all me states in the speaker independent models; means for determining an adaptation transformation using said speaker adaptation data and said models by evaluating how well adaptation features aligned to recognized states in said adaptation data are described by the corresponding states of said models and determining a transformation to improve the description by said models; means for applying said transformation to said complete set of speaker independent models to create a complete set of speaker adapted models; and means for using said speaker adapted models to recognize subsequent speech data from said speaker. 8. The device according to claim 7 wherein the probability functions are represented by a mixmre of simple probability functions and wherein said adapting means adapts die system to the speaker by transforming the simple probability functions. 9. The device according to claim 7 wherein the simple probability functions are Gaussians. 10. The device according to claim 7 wherein die speaker independent probability functions are Gaussians mixtures having die form of equation (3). 11. The device according to claim 10 wherein the speaker adapted probability functions have die form of equation (5). 12. The device according to claim 11 wherein die transformation is accomplished according a method comprising the steps of: initializing all transformations with A g (0) = I, b g (0) = 0, g = 1,..., N. Set k = 0. E-step: performing one iteration of a forward-backward algoridim on die speech data, using Gaussians transformed with die current value of die transformations A g (k), b g (k); collecting for all component Gaussians and all genones g die sufficient statistics: Si, - — ∑ Y Φβ ) ( *,-μ.p (*,- 7μ7< ■ * !" (28) n ig ***, = Σ YlrøΨlto (29) v, where (s) is die probability of being at state , at time t given the current HMM parameters λ k , die summation is over all times and HMM states mat share the same mixture components, and φ u (sj is die posterior probability ,„ Λ . Φ.W - P(ω I A g (k),b g (k),x t ,s) (30) M-step: computing die new transformation parameters [A g (k + 1), b g (k + 1) obtained by solving die following equations for each g (31) where die offset b is given by (where for simplicity we have dropped the dependence on g and where the variables μ„ σ 2 ! , "^ are the elements of the vectors and diagonal matrices μ^, ∑ v , " μ^, ∑j,, respectively; and if me convergence criterion is not met, going to E-step. 13. A speaker adaptive speech recognition system comprising: a set of models representing selected subunits of speech, each model having associated with it a plurality of states and each state having associated with it a probability function, die probability functions having parameters determined from traimng data; a plurality of codebooks, each codebook containing a set of simple probability functions; each codebook associated with a plurality of said states, die probability function of each one of said states being a weighted sum of the simple probability functions stored in its associated codebook; means for collecting a set of speaker adaptation data from a particular speaker, said set of adaptation data comprising words of die speaker's choice spoken by the speaker; means for determining a transformation using said speaker adaptation data and said models, said transformation capable of transforming said speaker independent models into a set of speaker adapted models; means for using said transformation on said set of models to create a set of speaker adapted models by applying a transformation derived from one state to the codebook associated with tiiat state, thereby transforming die model for all otiier states associated widi tiiat codebook; and means for applying said speaker adapted models to subsequent speech data from said speaker. 14. The device according to claim 13 wherein the probability functions are represented by a mixmre of simple probability functions and wherein said adapting means adapts die system to die speaker by transforming the simple probability functions. 15. The device according to claim 13 wherein the simple probability functions are Gaussians. 16. The device according to claim 13 wherein the probability functions are Gaussians mixtures having the die form of equation (3). 17. The device according to claim 13 wherein the speaker adapted probability functions have die form of equation (5). 18. In a speech recognition system for responding to signals representative of digital speech, a method for developing models adapted to an individual speaker comprising the steps of: selecting a multi-state model witii state probability functions, said probability functions being of a general form with initially undetermined parameters; creating an individual instance of a model for each subunit of speech to be processed; using training data from a plurality of speakers to determine acoustic features of states of said models and to estimate probability density functions for said models; clustering states based on tiieir acoustic similarity; creating a plurality of cluster codebooks, one codebook for each cluster; said cluster codebooks consisting of probability density functions that are shared by each cluster's states; reestimating the probability densities of each cluster codebook and die parameters of the probability equations in each cluster acquiring a set of speaker dependent training data from an individual speaker, said set consisting of words spoken at random by said individual speaker; recognizing said set of training data as being probably generated by particular states; and using he results of said recognizing to adapt said models to said particular speaker. 19. The method according to claim 18 wherein the simple probability functions are Gaussians. 20. The method according to claim 18 wherein the number of said clusters is an arbitrary number selected based on system resources and desired performance characteristics. 21. A speaker adaptive speech recognizer, comprising: a computer; storage means; a set of models for subunits of speech stored in die storage means; a feature extractor in die computer for extracting feature data capable of being processed by said computer from a speech signal; training means in the computer for training the models using features from identified samples of speech data and for producing a master codebook of probability density functions for use by die models; clustering means in the computer for identifying clusters of states that share subsets of the probability density functions in die codebooks; splitting and pruning means in the computer for producing cluster codebooks by splitting die master codebook into subsets of probability densities shared by clustered states; re-estimating means for retraining the models for the states in die clusters and for recalculating die probability densities in each cluster codebook; recognizing means for matching features from unidentified speech data to me models to produce a most likely path through die models where the padi defines die most likely subunits and words in die speech data.; and speaker adaptive means for adapting each cluster codebook to an individual speaker using a small amount of speaker adaptive data from said individual speaker. 22. The system to claim 1 further comprising: means for storing a plurality of speaker dependent models trained by die training engine for an individual speaker; means for combining parameters of said speaker adapted models widi parameters of said speaker dependent models to generate improved speaker dependent models. 23. The system according to claim 22 wherein said improved improved speaker dependent models have parameters derived according to equations of me form of equation (26). 24. A speach recognition system employing die method for adapting speech models illustrated in Fig. la. 25. A speach recognition system employing the method for adapting speech models illustrated in Fig. lc.

Inventors: DIGALAKIS VASSILIOS, NEUMEYER LEONARDO, RTISCHEV DIMITRY, DIGALAKIS, VASSILIOS, NEUMEYER, LEONARDO, RTISCHEV, DIMITRY
Applicants: STANFORD RES INST INT, SRI INTERNATIONAL
Application year: 1996
Main classification: G01L5
IPC codes: G10L15, G10L17