hiwire meeting nancy, july 6-7, 2006

HIWIRE MEETINGHIWIRE MEETINGNancy, July 6-7, 2006Nancy, July 6-7, 2006

José C. Segura, Ángel de la TorreJosé C. Segura, Ángel de la Torre

2 HIWIRE Meeting – Nancy, 6 -7 June, 2006

Schedule

Non-linear feature normalization for mobile platform Integration scheme Results and discussion

Rapid speaker adaptation Combination of adaptation at signal level and acoustic

model level Results and discussion

Assessment of two non-linear techniques for feature normalization Non-linear parametric equalization Model based feature compensation (VTS)

New improvements in robust VAD Model based VAD


José C. Segura,José C. Segura, Ángel de la Torre Ángel de la Torre


Schedule







Non-linear Parametric Equalization

Feature normalization

Motivation of PEQ: Limitation of linear methods:

Cepstral Mean Normalization Cepstral Mean and Variance Normalization

Limitation of non-linear methods (HEQ, OSEQ): Speech/non-speech ratio Estimation problems

Parametric Equalization PEQ: Two Gaussian Model (speech / non-speech) Training of clean Gaussians; estimation of noisy

Gaussians Non-linear transformation: combination of two linear

transformations (one for speech, one for non-speech)



Aurora-2 results:

Aver. WER Relative improv.

BASELINE 34.1 % 0.0 %

OSEQ 17.5 % 48.6 %

PEQ 18.6 % 45.3 %

Aurora-4 results:


BASELINE 45.6 % 0.0 %

OSEQ 37.5 % 17.8 %

PEQ 31.5 % 30.1 %



Additional problem of non-linear transformations: Once the transformation is estimated, it is an

“instantaneous transformation” Temporal correlations are not exploited

Temporal Smoothing (TES): Each equalized cepstrum is time-filtered with an ARMA

filter that restores autocorrelation of clean data



Aurora-2 results:

Aver. WER Improv. Aver. WER Improv.

BASELINE 34.1 % 0.0 % 31.6 % 6.5 %

OSEQ 17.5 % 48.6 % 15.5 % 54.3 %

PEQ 18.6 % 45.3 % --- ---

Aurora-4 results:

TES

Aver. WER Improv. Aver. WER Improv.

BASELINE 45.6 % 0.0 % 43.4 % 4.9 %

OSEQ 37.5 % 17.8 % 35.5 % 22.2 %

PEQ 31.5 % 30.1 % 30.7 % 32.6 %

TES


Model Based Feature Compensation (VTS)

VTS feature normalization: Performed in log-FBE domain, (previous to DCT) Based on a Gaussian mixture model trained with clean

speech Allows feature compensation and uncertainty estimation

Summary of VTS (vector Taylor series approach):1. Given the noisy conditions, VTS provides a noisy

Gaussian from each clean Gaussian

2. The noisy Gaussian mixture model allow the computation of the probabilities P(k|y)

3. An estimation of the clean speech x is then possible

4. An estimation of the uncertainty is also possible



Step 1: Estimation of a noisy Gaussian from a clean Gaussian:

where the function g0, f0 and h0 are evaluated at the mean of the clean Gaussian and at the mean of the noise:



Step 2: Estimation of P(k|y):

is the k-th Gaussian evaluated at the noisy speech y, and P(k) is the a-priori probability of the Gaussian.

where:

Step 3: Estimation of clean speech:



Step 4: Estimation of uncertainty:

the uncertainty of the clean speech can be estimated as:

and from the estimation of the clean speech:

assuming small values of the variance of the noise:


Aurora-2 results:


BASELINE 34.1 % 0.0 %

VTS + MVN 14.0 % 58.9 %

VTS + MVN + UNCERT. 13.5 % 60.0 %


Some considerations about VTS: Computational load Better than HEQ, PEQ, etc., but only valid for additive noise or

channel distortion Estimation of noise is critical There are some approximations in the formulation Uncertainty: small improvement (insert., substit., delet.)

Alternative: model-based compensation based on numerical integration of pdfs


Schedule







Model-based VAD

Fundamentals of model-based VAD: Gaussian mixture model in log-FBE domain Gaussian mixture model trained with clean speech VTS provides a noisy version of the GMM From the noisy GMM, P(k|y) can be estimated for

each observation y and each Gaussian k A-priori probability of kth Gaussian being speech

P(V|k) can be estimated from the training data

Then, the probability P(V|y) of the noisy observation y being speech is given by:


Model-based VAD

Some considerations about model-based VAD:

VAD decision relies on a Gaussian mixture model trained with clean speech (based on speech events observed in the training database)

Not based on energy.... Based on observations in the log-FBE domain

VTS adapts the Gaussian mixture to noisy conditions: the performance of the VAD is expected to be stable for a wide range of SNRs

Computational load


Model-based VAD

Model-based VAD for different SNRs:


Model-based VAD

Comparison with other VADs: HR1 and HR0 evaluated for AURORA-2


Aurora-2 recognition results (WAcc):

Model-based VAD

WF WF+FD

G.729 57.1 % 57.8 %

AMR.1 66.3 % 65.0 %

AMR.2 78.3 % 78.5 %

AFE 75.3 % 79.0 %

VTS-VAD 78.4 % 80.2 %

Baseline: 60.5 % (no VAD, no WF, no FD)


José C. Segura, Ángel de la TorreJosé C. Segura, Ángel de la Torre

hiwire meeting nancy, july 6-7, 2006

Documents

hiwire meeting nancy

nonlinear techniques

clean speech x

acoustic model levelresults

limitation of linear

noisy conditions

autocorrelation of clean

possiblean estimation