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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.
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16/05/2003 Reunion Bayestic / Murat Deviren 1
Reunion Bayestic
Excuse moi!
Murat Deviren
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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
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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
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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
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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
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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
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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.
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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
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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
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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.
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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
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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 ||.||
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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)
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16/05/2003 Reunion Bayestic / Murat Deviren 28
Les modèles statistiques pour la transformée ondelette
t
f
t
f
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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
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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’