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Reunion Bayestic

Reunion Bayestic. Excuse moi! Murat Deviren. Contents. Frequency and wavelet filtering Supervised-predictive compensation Language modeling with DBNs Hidden Markov Trees for acoustic modeling. Contents. Frequency and wavelet filtering Supervised-predictive compensation

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Reunion Bayestic

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  1. Reunion Bayestic Excuse moi! Murat Deviren Reunion Bayestic / Murat Deviren

  2. Contents • Frequency and wavelet filtering • Supervised-predictive compensation • Language modeling with DBNs • Hidden Markov Trees for acoustic modeling Reunion Bayestic / Murat Deviren

  3. Contents • Frequency and wavelet filtering • Supervised-predictive compensation • Language modeling with DBNs • Hidden Markov Trees for acoustic modeling Reunion Bayestic / Murat Deviren

  4. Proposed by Nadeu’95, Paliwal’99. Goal : Spectral features comparable with MFCCs Properties : Quasi decorrelation of logFBEs. Cepstral weighting effect Emphasis on spectral variations DCT MFCC logFBEs H(z) FF H(z) = 1-az-1 Frequency Filtering Simplified block diagram for MFCC and FF parameterizations Typical derivative type frequency filters Reunion Bayestic / Murat Deviren

  5. Significant performance decrease for FF2 & FF3 in high mismatch case Evaluation of FF on Aurora-3 Reunion Bayestic / Murat Deviren

  6. FF1 = Haar Wavelet Reformulate FF as wavelet filtering Use higher order Daubechies wavelets Promising results Published in ICANN 2003 Wavelets and Frequency Filtering Reunion Bayestic / Murat Deviren

  7. Perspectives • BUT • These results could not be verified on other subsets of Aurora-3 database. • To Do • Detailed analysis of FF and wavelet filtering • Develop models that exploit frequency localized features. • Exploit statistical properties of wavelet transform. Reunion Bayestic / Murat Deviren

  8. Contents • Frequency and wavelet filtering • Supervised-predictive compensation • Language modeling with DBNs • Hidden Markov Trees for acoustic modeling Reunion Bayestic / Murat Deviren

  9. Noise Robustness • Signal processing techniques : • CMN, RASTA, enhancement techniques • Compensation schemes • Adaptive : MLLR, MAP • Requires adaptation data and a canonical model • Predictive : PMC • Hypothetical errors in mismatch function • Strong dependence on front-end parameterization • Multi-condition training Reunion Bayestic / Murat Deviren

  10. Supervised-predictive compensation • Goal : • exploit available data to devise a tool for robustness. • Available data : • speech databases recorded in different acoustic environments. • Principles : • Train matched models for each condition. • Train noise models. • Construct a parametric model that describe how matched models vary with noise model. Reunion Bayestic / Murat Deviren

  11. Supervised-predictive compensation • Advantages : • No mismatch function • Independent of front-end • Canonical model is not required • Computationally efficient • Model can be trained incrementally • i.e. can be updated with new databases Reunion Bayestic / Murat Deviren

  12. Deterministic model • Databases : D1, …, DK • Noise conditions : n1, …, nK • Sw(k) : matched speech model for acoustic unit wW trained on noise condition nk. • N{1,…, K}: noise variable. • For each wW, there exists a parametric function fw such that • || Sw(k) – fw(N) ||  0 for some given norm ||.|| Reunion Bayestic / Murat Deviren

  13. N1 S1 N2 S2 N3 S3 Probabilistic model • Given • S : speech model parameterization • N : noise model parameterization • Learn the joint probability density P(S, N) • Given the noise model N, what is the best set of speech models to use? • S` = argmax P(S|N) P(S,N) as a static Bayesian network S N Reunion Bayestic / Murat Deviren

  14. A simple linear model • Speech model : mixture density HMM • Noise model : single Gaussian • wls(nk) = Awlsnk + Bwls • wls(nk) : mean vector for mixture component l ofstate s • nk: mean vector of noise model • fw is parameterized with Awls, Bwls • Supervised training using MMSE minimization Reunion Bayestic / Murat Deviren

  15. Experiments • Connected digit recognition on TiDigits • 15 different noise sources from NOISEX • volvo, destroyer engine, buccaneer…. • Evaluations : • Model performance in training conditions • Robustness comparison with multi-condition training : • under new SNR conditions, • under new noise types. Reunion Bayestic / Murat Deviren

  16. Results • Even a simple linear model can almost recover matched model performances. • The proposed technique can generalize to new SNR conditions and new noise types. • Results submitted to EUROSPEECH 2003 Reunion Bayestic / Murat Deviren

  17. Contents • Frequency and wavelet filtering • Supervised-predictive compensation • Language modeling with DBNs • Hidden Markov Trees for acoustic modeling Reunion Bayestic / Murat Deviren

  18. Classical n-grams • Word probability based on word history. • P(W) = iP(wi | wi-1, wi-2, … , wi-n) wi-n wi-2 wi-1 wi Reunion Bayestic / Murat Deviren

  19. Class based n-grams • Class based word probability for a given class history. • P(W) = iP(wi | ci) P(ci | ci-1, ci-2, … , ci-n) ci-n ci-2 ci-1 ci wi-n wi-2 wi-1 wi Reunion Bayestic / Murat Deviren

  20. Class based LM with DBNs • Class based word probability in a given class context. • P(W) = iP(wi | ci-n, …, ci,…ci+n) P(ci | ci-1, ci-2, … , ci-n) ci-n ci-2 ci-1 ci ci+1 ci+2 wi-n wi-2 wi-1 wi Reunion Bayestic / Murat Deviren

  21. Initial results • Training corpus 11 months from le monde ~ 20 million words • Test corpus ~ 1.5 million words • Vocabulary size : 500 • # class labels = 198 wi-1 wi ci-1 ci wi ci-1 ci wi ci-1 ci ci+1 wi Reunion Bayestic / Murat Deviren

  22. Perspectives • Initial results are promising. • To Do • Learning structure with appropriate scoring metric, i.e., based on perplexity • Appropriate back-off schemes • Efficient CPT representations for computational constraints, i.e., noisy-OR gates. Reunion Bayestic / Murat Deviren

  23. Contents • Frequency and wavelet filtering • Supervised-predictive compensation • Language modeling with DBNs • Hidden Markov Trees for acoustic modeling Reunion Bayestic / Murat Deviren

  24. Reconnaissance de la parole à l’aide de modèles de Markov cachés sur des arbres d’ondelettes Sanaa GHOUZALI DESA Infotelecom Université Med V - RABAT Reunion Bayestic / Murat Deviren

  25. Problèmes de la reconnaissance de la parole • Paramétrisation: • Besoin de localiser les paramètres du signal parole dans le domaine temps-fréquence • Avoir des performances aussi bonnes que les MFCC • Modélisation: • Besoin de construire des modèles statistiques robuste au bruit • Besoin de modéliser les dynamiques fréquentielles du signal parole aussi bien que les dynamiques temporelles Reunion Bayestic / Murat Deviren

  26. Paramètrisation • La transformée Ondelette a de nombreuses propriétés intéressantes qui permettent une analyse plus fine que la transformée Fourrier; • Localité • Multi-résolution • Compression • Clustering • Persistence Reunion Bayestic / Murat Deviren

  27. Modélisation • Il existe plusieurs types de modèles statistiques qui tiennent compte des propriétés de la transformée ondelette; • Independent Mixtures (IM): traite chaque coefficient indépendamment des autres (pptés primaire) • Markov chains: considère seulement les corrélations entre les coefficients dans le temps (clustering) • Hidden Markov Trees (HMT): considère les corrélations entre échelles (persistence) Reunion Bayestic / Murat Deviren

  28. t t f f Les modèles statistiques pour la transformée ondelette Reunion Bayestic / Murat Deviren

  29. Description du modèle choisi • le modèle choisi WHMT : • illustre bien les propriété clustering et persistance de la transformée ondelette • interprète les dépendances complexes entre les coefficients d'ondelette • la modélisation pour la transformée ondelette sera faite en deux étapes: • modéliser chaque coefficient individuellement par un modèle de mélange de gaussienne • capturer les dépendances entre ces coefficients par le biais du modèle HMT Reunion Bayestic / Murat Deviren

  30. Références M. S. Crouse, R. D. Nowak, and R. G. Baraniuk, ‘Wavelet-Based Statistical Signal- Processing Using Hidden Markov Models’, IEEE Trans. Signal. Proc., vol. 46 , no. 4, pp. 886-902, Apr. 1998 M. Crouse, H. Choi and R. Baraniuk, ‘Multiscale Statistical Image Processing Using Tree-Structured Probability Models’, IT Workshop, Feb. 1999 K. Keller, S. Ben-Yacoub, and C. Mokbel, ‘Combining Wavelet-Domain Hidden Markov Trees With Hidden Markov Models’, IDIAP-RR 99-14, Aug. 1999 M. Jaber Borran and R. D. Nowak, ‘Wavelet-Based Denoising Using Hidden Markov Models’ Reunion Bayestic / Murat Deviren

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