1. Applications of Risk Minimization to Speech Recognition Joseph Picone Inst. for Signal and Info. Processing Dept. Electrical and Computer Eng. Mississippi State University Contact Information: Box 9571 Mississippi State University Mississippi State, Mississippi 39762 Tel: 662-325-3149 Fax: 662-325-2298 Email: picone@isip.msstate.edupicone@isip.msstate.edu IBM – SIGNAL PROCESSING URL: www.isip.msstate.edu/publications/seminars/msstate_misc/2004/cse Acknowledgement: Supported by NSF under Grant No. EIA-9809300.

2. INTRODUCTION ABSTRACT AND BIOGRAPHY ABSTRACT: Statistical techniques based on Hidden Markov models (HMMs) with Gaussian emission densities have dominated the signal processing and pattern recognition literature for the past 20 years. However, HMMs suffer from an inability to learn discriminative information and are prone to overfitting and over ‑ parameterization. In this presentation, we will review our attempts to apply notions of risk minimization into pattern recognition problems such as speech recognition. New approaches based on probabilistic Bayesian learning are shown to provide an order of magnitude reduction in complexity over comparable approaches based on HMMs and Support Vector Machines. BIOGRAPHY: Joseph Picone is currently a Professor in the Department of Electrical and Computer Engineering at Mississippi State University, where he also directs the Institute for Signal and Information Processing. For the past 15 years he has been promoting open source speech technology. He has previously been employed by Texas Instruments and AT&T Bell Laboratories. Dr. Picone received his Ph.D. in Electrical Engineering from Illinois Institute of Technology in 1983. He is a Senior Member of the IEEE and a registered Professional Engineer.

3. HUMAN LANGUAGE TECHNOLOGY SPEECH RECOGNITION RESEARCH? Why do we work on speech recognition? “Language is the preeminent trait of the human species.” “I never met someone who wasn’t interested in language.” “I decided to work on language because it seemed to be the hardest problem to solve.” Why should we work on speech recognition? Antiterrorism, homeland security, military applications Telecommunications, mobile communications Education, learning tools, educational toys, enrichment Computing, intelligent systems, machine learning Commodity or liability? Fragile technology that is error prone

4. INTRODUCTION GENERALIZATION AND RISK Optimal decision surface is a line Optimal decision surface changes abruptly Optimal decision surface still a line How much can we trust isolated data points? Can we integrate prior knowledge about data, confidence, or willingness to take risk?

5. HUMAN LANGUAGE TECHNOLOGY FUNDAMENTAL CHALLENGES

6. INTRODUCTION ACOUSTIC CONFUSABILITY Regions of overlap represent classification error Reduce overlap by introducing acoustic and linguistic context Comparison of “aa” in “lOck” and “iy” in “bEAt” for conversational speech

7. INTRODUCTION PROBABILISTIC FRAMEWORK

8. SPEECH RECOGNITION BLOCK DIAGRAM OVERVIEW Core components: transduction feature extraction acoustic modeling (hidden Markov models) language modeling (statistical N-grams) search (Viterbi beam) knowledge sources

9. Maximum likelihood convergence does not translate to optimal classification if a priori assumptions about the data are not correct. Finding the optimal decision boundary requires only one parameter. INTRODUCTION ML CONVERGENCE NOT OPTIMAL

10. INTRODUCTION POOR GENERALIZATION WITH GMM MLE Data is often not separable by a hyperplane – nonlinear classifier is needed Gaussian MLE models tend toward the center of mass – overtraining leads to poor generalization Three problems: controlling generalization, direct discriminative training, and sparsity.

11. RISK MINIMIZATION DISCRIMINATIVE TRAINING Several popular discriminative training approaches (e.g., maximum mutual information estimation) Essential Idea: Maximize Maximize numerator (ML term), minimize denominator (discriminative term) Previously developed for neural networks, hybrid systems, and eventually HMM-based speech recognition systems

12. Structural optimization often guided by an Occam’s Razor approach Trading goodness of fit and model complexity –Examples: MDL, BIC, AIC, Structural Risk Minimization, Automatic Relevance Determination RISK MINIMIZATION Model Complexity Error Training Set Error Open-Loop Error Optimum STRUCTURAL OPTIMIZATION

13. RISK MINIMIZATION SVMS FOR NON-SEPARABLE DATA No hyperplane could achieve zero empirical risk (in any dimension space!) Recall the SRM Principle: balance empirical risk and model complexity Relax our optimization constraint to allow for errors on the training set: A new parameter, C, must be estimated to optimally control the trade-off between training set errors and model complexity

14. RISK MINIMIZATION DRAWBACKS OF SVMS Uses a binary (yes/no) decision rule  Generates a distance from the hyperplane, but this distance is often not a good measure of our “confidence” in the classification  Can produce a “probability” as a function of the distance (e.g. using sigmoid fits), but they are inadequate Number of support vectors grows linearly with the size of the data set Requires the estimation of trade-off parameter, C, via held-out sets

15. Build a fully specified probabilistic model – incorporate prior information/beliefs as well as a notion of confidence in predictions MacKay posed a special form for regularization in neural networks – sparsity Evidence maximization: evaluate candidate models based on their “evidence”, P(D|H i ) Structural optimization by maximizing the evidence across all candidate models Steeped in Gaussian approximations RELEVANCE VECTOR MACHINES EVIDENCE MAXIMIZATION

16. A kernel-based learning machine Incorporates an automatic relevance determination (ARD) prior over each weight (MacKay) A flat (non-informative) prior over  completes the Bayesian specification RELEVANCE VECTOR MACHINES AUTOMATIC RELEVANCE DETERMINATION

17. The goal in training becomes finding: Estimation of the “sparsity” parameters is inherent in the optimization – no need for a held-out set! A closed-form solution to this maximization problem is not available. Iteratively reestimate RELEVANCE VECTOR MACHINES ITERATIVE REESTIMATION

18. Fix  and estimate w (e.g. gradient descent) Use the Hessian to approximate the covariance of a Gaussian posterior of the weights centered at With and as the mean and covariance, respectively, of the Gaussian approximation, we find by finding Method is O(N 2 ) in memory and O(N 3 ) in time RELEVANCE VECTOR MACHINES LAPLACE’S METHOD

19. RVM: Data: Class labels (0,1) Goal: Learn posterior, P(t=1|x) Structural Optimization: Hyperprior distribution encourages sparsity Training: iterative O(N 3 ) SVM: Data: Class labels (-1,1) Goal: Find optimal decision surface under constraints Structural Optimization: Trade-off parameter that must be estimated Training: Quadratic O(N 2 ) RELEVANCE VECTOR MACHINES COMPARISON TO SVMS

20. Deterding Vowel Data: 11 vowels spoken in “h*d” context; 10 log area parameters; 528 train, 462 SI test Approach% Error# Parameters SVM: Polynomial Kernels49% K-Nearest Neighbor44% Gaussian Node Network44% SVM: RBF Kernels35%83 SVs Separable Mixture Models30% RVM: RBF Kernels30%13 RVs EXPERIMENTAL RESULTS DETERDING VOWEL DATA

21. Data size: –30 million frames of data in training set –Solution: Segmental phone models Source for Segmental Data: –Solution: Use HMM system in bootstrap procedure –Could also build a segment- based decoder Probabilistic decoder coupling: –SVMs: Sigmoid-fit posterior –RVMs: naturally probabilistic EXPERIMENTAL RESULTS INTEGRATION WITH SPEECH RECOGNITION hhawaaryuw region 1 0.3*k frames region 3 0.3*k frames region 2 0.4*k frames mean region 1mean region 2mean region 3 k frames

22. SEGMENTAL CONVERTER SEGMENTAL CONVERTER HMM RECOGNITION HMM RECOGNITION HYBRID DECODER HYBRID DECODER Features (Mel-Cepstra)) Segment Information N-best List Segmental Features Hypothesis EXPERIMENTAL RESULTS HYBRID DECODER

23. HMM system is cross-word state-tied triphones with 16 mixtures of Gaussian models SVM system has monophone models with segmental features System combination experiment yields another 1% reduction in error EXPERIMENTAL RESULTS SVM ALPHADIGIT RECOGNITION TranscriptionSegmentationSVMHMM N-bestHypothesis11.0%11.9% N-best+RefReference3.3%6.3%

24. RVMs yield a large reduction in the parameter count while attaining superior performance Computational costs mainly in training for RVMs but is still prohibitive for larger sets ApproachError Rate Avg. # Parameters Training Time Testing Time SVM16.4%2570.5 hours30 mins RVM16.2%1230 days1 min EXPERIMENTAL RESULTS SVM/RVM ALPHADIGIT COMPARISON

25. SUMMARY PRACTICAL RISK MINIMIZATION? Reduction of complexity at the same level of performance is interesting: Results hold across tasks RVMs have been trained on 100,000 vectors Results suggest integrated training is critical Risk minimization provides a family of solutions: Is there a better solution than minimum risk? What is the impact on complexity and robustness? Applications to other problems? Speech/Non-speech classificiation? Speaker adaptation? Language modeling?

26. Traditional Output: best word sequence time alignment of information Other Outputs: word graphs N-best sentences confidence measures metadata such as speaker identity, accent, and prosody SPEECH RECOGNITION APPLICATION OF INFORMATION RETRIEVAL

27. APPLICATIONS INFORMATION RETRIEVAL Metadata extraction from conversational speech Automatic gisting and intelligence gathering Speech to text is the core technology challenge Machines vs. humans Real-time audio indexing Time-varying channel Dynamic language model Multilingual and cross-lingual

28. APPLICATIONS In-vehicle dialog systems improve information access. Advanced user interfaces enhance workforce training and increase manufacturing efficiency. Noise robustness in both environments to improve recognition performance Advanced statistical models and machine learning technology Multidisciplinary team (IE, ECE, CS). CAVS: DIALOG SYSTEMS FOR THE CAR

29. Principal Investigators: Aravind Ganapathiraju (Conversay) and Jon Hamaker (Microsoft) as part of their Ph.D. studies at Mississippi State Consultants: Michael Tipping (MSR-Cambridge) and Thorsten Joachims (Cornell) Motivation: Serious work began after discussions with V.N. Vapnik at the CLSP Summer Workshop in 1997. SUMMARY ACKNOWLEDGEMENTS

30. SUMMARY RELEVANT SOFTWARE RESOURCES Pattern Recognition Applet: compare popular algorithms on standard or custom data sets Speech Recognition Toolkits: compare SVMs and RVMs to standard approaches using a state of the art ASR toolkit Fun Stuff: have you seen our commercial on the Home Shopping Channel? Foundation Classes: generic C++ implementations of many popular statistical modeling approaches

31. SUMMARY BRIEF BIBLIOGRAPHY Applications to Speech Recognition: 1.J. Hamaker and J. Picone, “Advances in Speech Recognition Using Sparse Bayesian Methods,” submitted to the IEEE Transactions on Speech and Audio Processing, January 2003.Advances in Speech Recognition Using Sparse Bayesian Methods 2.A. Ganapathiraju, J. Hamaker and J. Picone, “Applications of Risk Minimization to Speech Recognition,” submitted to the IEEE Transactions on Signal Processing, July 2003.Applications of Risk Minimization to Speech Recognition 3.J. Hamaker, J. Picone, and A. Ganapathiraju, “A Sparse Modeling Approach to Speech Recognition Based on Relevance Vector Machines,” Proceedings of the International Conference of Spoken Language Processing, vol. 2, pp. 1001-1004, Denver, Colorado, USA, September 2002.A Sparse Modeling Approach to Speech Recognition Based on Relevance Vector Machines 4.J. Hamaker, Sparse Bayesian Methods for Continuous Speech Recognition, Ph.D. Dissertation, Department of Electrical and Computer Engineering, Mississippi State University, December 2003.Sparse Bayesian Methods for Continuous Speech Recognition 5.A. Ganapathiraju, Support Vector Machines for Speech Recognition, Ph.D. Dissertation, Department of Electrical and Computer Engineering, Mississippi State University, January 2002.Support Vector Machines for Speech Recognition Influential work: 6.M. Tipping, “Sparse Bayesian Learning and the Relevance Vector Machine,” Journal of Machine Learning, vol. 1, pp. 211-244, June 2001. 7.D. J. C. MacKay, “Probable networks and plausible predictions --- a review of practical Bayesian methods for supervised neural networks,” Network: Computation in Neural Systems, 6, pp. 469-505, 1995. 8.D. J. C. MacKay, Bayesian Methods for Adaptive Models, Ph. D. thesis, California Institute of Technology, Pasadena, California, USA, 1991. 9.E. T. Jaynes, “Bayesian Methods: General Background,” Maximum Entropy and Bayesian Methods in Applied Statistics, J. H. Justice, ed., pp. 1-25, Cambridge Univ. Press, Cambridge, UK, 1986. 10.V.N. Vapnik, Statistical Learning Theory, John Wiley, New York, NY, USA, 1998. 11.V.N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, NY, USA, 1995. 12.C.J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” AT&T Bell Laboratories, November 1999.

32. CASE STUDIES Introduced at the Summer Consumer Electronics Show in Chicago First commercial speech synthesis consumer toy Based on linear prediction Contained a proprietary speech synthesis chip SPEAK & SPELL™ (JUNE 1978) Left to right: Gene Frantz, Richard Wiggins, Paul Breedlove and Larry Brantingham (1978)

33. CASE STUDIES Yes / no / true / false recognizer Answer questions about history Variety of learning modules Speaker independent recognition Microphone + children??? Won several industry design awards for the mechanical design VOYAGER™ (JUNE 1988)

34. CASE STUDIES Worlds of Wonder approach TI in September of 1988. Can you put this toy on the market by Thanksgiving? 10-word speaker dependent isolated word recognizer 100 sentences for synthesis “Transparent training” First large-scale consumer toy application for a DSP JULIE (DECEMBER 1988)

35. CASE STUDIES Voice verification for calling card security First wide-spread deployment of recognition technology in the telephone network Stimulated interest in voice dialing and other user- programmable features Original application was obsolete before wide-scale deployment WATSON (EARLY 1990’S)