16/05/2003reunion bayestic / murat deviren1 reunion bayestic excuse moi! murat deviren

16/05/2003 Reunion Bayestic / Murat Deviren 1

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 Filtering• Proposed by Nadeu’95,

Paliwal’99.

• Goal : Spectral features comparable with MFCCs

• Properties :– Quasi decorrelation of

logFBEs.

– Cepstral weighting effect

– Emphasis on spectral variations

FF1 FF2 FF3

H(z) 1-z-1 z-z-1 1-z-2

logFBEs

DCT

H(z)

MFCC

FF

H(z) = 1-az-1

Simplified block diagram for MFCC and FF parameterizations

Typical derivative type frequency filters


Evaluation of FF on Aurora-3

• Significant performance decrease for FF2 & FF3 in high mismatch case

FF1 FF2 FF3

H(z) 1-z-1 z-z-1 1-z-2

0

20

40

60

80

100

Aurora-3 German database

MFCC FF1 FF2 FF3

MFCC 90.58 79.06 74.28

FF1 90.8 79.8 73.17

FF2 90.1 79.21 64.8

FF3 89.68 78.62 63.78

WM MM HM


Wavelets and Frequency Filtering

• FF1 = Haar Wavelet• Reformulate FF as

wavelet filtering• Use higher order

Daubechies wavelets

• Promising results• Published in ICANN 2003

0

20

40

60

80

100

Aurora-3 German database

MFCC FF1 FF2 FF3 Daub4

MFCC 90.58 79.06 74.28

FF1 90.8 79.8 73.17

FF2 90.1 79.21 64.8

FF3 89.68 78.62 63.78

Daub4 90.3 76.94 78.21

WM MM HM


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.


Contents






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


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.


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


Deterministic model

• Databases : D1, …, DK

• Noise conditions : n1, …, nK

• Sw(k) : matched speech model for acoustic unit wW trained on noise condition nk.

• N{1,…, K}: noise variable.• For each wW, there exists a parametric

function fw such that

– || Sw(k) – fw(N) || 0 for some given norm ||.||


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)

S1

S2

S3

N1

N2

N3

N S

P(S,N) as a staticBayesian network


A simple linear model

• Speech model : mixture density HMM• Noise model : single Gaussian wls(nk) = Awlsnk + Bwls

wls(nk) : mean vector for mixture component l of state s

nk : mean vector of noise model

• fw is parameterized with Awls, Bwls

• Supervised training using MMSE minimization


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.


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


Contents






Classical n-grams

• Word probability based on word history.

• P(W) = i P(wi | wi-1, wi-2, … , wi-n)

wi-n wi-2 wiwi-1


Class based n-grams• Class based word probability for a given class

history.

• P(W) = i P(wi | ci) P(ci | ci-1, ci-2, … , ci-n)

ci-n ci-2 cici-1

wi-n wi-2 wiwi-1


Class based LM with DBNs• Class based word probability in a given class

context.

• P(W) = i P(wi | ci-n, …, ci,…ci+n)

P(ci | ci-1, ci-2, … , ci-n)

ci-n ci-2 cici-1

wi-n wi-2 wiwi-1

ci+1 ci+2


Initial results

• Training corpus 11 months from le monde~ 20 million words

• Test corpus~ 1.5 million words

• Vocabulary size : 500• # class labels = 198

wiwi-1

cici-1

wi

cici-1

wi

cici-1

wi

ci+1

Model Perplexity

47.80

38.26

32.80

33.37


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.


Contents






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


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


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


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)


Les modèles statistiques pour la transformée ondelette

t

f

t

f


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


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’

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