16/05/2003Reunion Bayestic / Murat Deviren1 Reunion Bayestic Excuse moi! Murat Deviren.
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Transcript of 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
16/05/2003 Reunion Bayestic / Murat Deviren 2
Contents
• Frequency and wavelet filtering
• Supervised-predictive compensation
• Language modeling with DBNs
• Hidden Markov Trees for acoustic modeling
16/05/2003 Reunion Bayestic / Murat Deviren 3
Contents
• Frequency and wavelet filtering
• Supervised-predictive compensation
• Language modeling with DBNs
• Hidden Markov Trees for acoustic modeling
16/05/2003 Reunion Bayestic / Murat Deviren 4
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
16/05/2003 Reunion Bayestic / Murat Deviren 5
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
16/05/2003 Reunion Bayestic / Murat Deviren 6
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
16/05/2003 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.
16/05/2003 Reunion Bayestic / Murat Deviren 8
Contents
• Frequency and wavelet filtering
• Supervised-predictive compensation
• Language modeling with DBNs
• Hidden Markov Trees for acoustic modeling
16/05/2003 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
16/05/2003 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.
16/05/2003 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
16/05/2003 Reunion Bayestic / Murat Deviren 12
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 ||.||
16/05/2003 Reunion Bayestic / Murat Deviren 13
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
16/05/2003 Reunion Bayestic / Murat Deviren 14
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
16/05/2003 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.
16/05/2003 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
16/05/2003 Reunion Bayestic / Murat Deviren 17
Contents
• Frequency and wavelet filtering
• Supervised-predictive compensation
• Language modeling with DBNs
• Hidden Markov Trees for acoustic modeling
16/05/2003 Reunion Bayestic / Murat Deviren 18
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
16/05/2003 Reunion Bayestic / Murat Deviren 19
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
16/05/2003 Reunion Bayestic / Murat Deviren 20
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
16/05/2003 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
wiwi-1
cici-1
wi
cici-1
wi
cici-1
wi
ci+1
Model Perplexity
47.80
38.26
32.80
33.37
16/05/2003 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.
16/05/2003 Reunion Bayestic / Murat Deviren 23
Contents
• Frequency and wavelet filtering
• Supervised-predictive compensation
• Language modeling with DBNs
• Hidden Markov Trees for acoustic modeling
16/05/2003 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
16/05/2003 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
16/05/2003 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
16/05/2003 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)
16/05/2003 Reunion Bayestic / Murat Deviren 28
Les modèles statistiques pour la transformée ondelette
t
f
t
f
16/05/2003 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
16/05/2003 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’