Download ppt - HIWIRE MEETING CRETE, SEPTEMBER 23-24, 2004 JOSÉ C. SEGURA LUNA GSTC UGR

HIWIRE MEETINGHIWIRE MEETINGCRETE, SEPTEMBER 23-24, 2004CRETE, SEPTEMBER 23-24, 2004

JOSÉ C. SEGURA LUNAJOSÉ C. SEGURA LUNA

GSTC UGRGSTC UGR

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HIWIRE Meeting – Crete, 23-24 September, 2004

Schedule

VAD for noise suppression & frame-dropping Long-Term Spectral divergence Subband OS-based detector

Non-linear feature normalization Histogram equalization OS-based equalization Segmental implementation

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VAD (1)

VAD: motivation To get an estimation of the background noise for

Wiener filter design Spectral subtraction

To discard non-speech frames

WIENERFILTER / SS

VAD

FRAMEDROPPING

NOISEESTIMATION

RECOGNIZERNOISY SPEECH

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VAD (2)

Our approach

Use of rather long time spans (~100ms) instead of instantaneous measures

Increase discrimination

Use an statistical model in the log-FBE domain Smoother estimations

Use a feedback decision coupled with noise suppression VAD works on less noisy speech

Use of Order Statistics More robust estimation

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Long-Term Spectral Divergence (1)

J. Ramírez , J.C. Segura, C. Benítez, A. de la Torre and A.J. Rubio, Efficient voice activity detection algorithms using long-term speech information, Speech Communication 42 (2004) 271–287

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Recognition experiments with AURORA 2 and 3

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Subband OSF VAD (1)

J. Ramírez, J.C. Segura, C. Benítez, A. de la Torre, and A.J. Rubio,An Effective Subband OSF-based VAD with Noise Reduction for Robust Speech Recognition, IEEE Trans. On Speech and Audio Processing (to appear in 2005)

Decision is based on averaged QSNR defined as a inter-quantile difference

Feedback structure VAD operates over the

noise-reduced signal

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Subband OSF VAD (2)

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Subband OSF VAD (3)

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Subband OSF VAD (4)

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Subband OSF VAD (5)

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Accurate VAD

Open topics

New alternatives to improve the performanceNew decision criteria based on OS- filtersAlready used for edge detection in images

Computational efficiencyDevelopment of computationally efficient algorithms

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Feature normalization

Objective Transform features to remove undesired variability

Linear techniques CMS

Cepstral mean subtraction Removes the effect of linear channel distortion

CMVN Cepstral mean and variance normalization Extension of CMS to deal with variance reduction caused by the

additive noise

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Non-linear feature distortion Environment effects are non-linear for MFCC features And can hardly be removed with linear techniques Because not only the location (mean) and scale (variance) of

the feature distributions are affected, but also the shape (affecting higher order moments of the distribution)

Non-linear extensions CDF-matching approaches (HEQ and related) Have been proved to be more effective than linear ones Give normalization for not only the two first moments of the

probability distributions

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CDF-matching based equalization

The main idea Transform the features to match a given PDF In the one-dimensional case CDF-matching gives the solution

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Equalization and robust classifiers

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Invariance

CMS is invariant to additive bias CMVN is invariant to linear transformations Equalization to a reference distribution is invariant to any

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HEQ for robust speech recognition (1)

A. de la Torre, A.M. Peinado, J.C. Segura, J.L. Pérez, C. Benítez and A.J. Rubio, Histogram equalization of speech representation for robust speech recognition, IEEE Tans. On Speech and Audio Processing (to appear in 2005)

Transformation of each component of the MFCC vector to a Gaussian reference

Cumulative distribution are estimated using histograms

Performance compared with CMS, CMVN and model-based feature compensation (VTS)

Combination with (VTS)

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Segmental HEQ (1)

J.C. Segura, C. Benítez, A. de la Torre, A.J. Rubio and J. Ramírez, Cepstral Domain Segmental Nonlinear Feature Transformations for Robust Speech Recognition, IEEE Signal Processing Letters, 11(5), May 2004

A segmental implementation of HEQ for non-stationary noise

A temporal buffer is used for the histogram estimation instead of the full sentence

The algorithmic delay is T frames

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Segmental HEQ (2)

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OSEQ: An efficient implementation (1)

A very computationally efficient algorithm based on Order Statistics

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OSEQ: An efficient implementation (2)

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Open topics Reference distribution

Clean speech / Gaussian / ¿Others? Dynamic features normalization ( and )

After, before or simultaneously [Obuchi, Stern, EUSP’03]

Progressive normalization Not all MFCC are equally affected and do not have equal

discriminative power [de Wet, …, ICASSP’03] Lower order moments normalization [Hsu, Lee, ICASSP’04]

Parametric techniques Actual approaches are non-parametric [Haverinen, Kiss, EUSP’03]

New applications Speaker independence and adaptation Multi-stream normalization

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Combination of techniques

Development of a combined robust front-end

An accurate VAD For noise parameter estimation

A noise reduction technique Spectral subtraction or Wiener filter Statistical feature compensation

A Frame-Dropping algorithm To discard non-speech frames

And a Feature normalization block For residual non-linear distortion compensation

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VAD (1)

Development of a combined robust front-end

WIENERFILTER / SS

VAD

FRAMEDROPPING

NOISEESTIMATION

FEATUREEQUALIZATION

NOISY SPEECHRECOGNIZER

HIWIRE MEETINGHIWIRE MEETINGCRETE, SEPTEMBER 23-24, 2004CRETE, SEPTEMBER 23-24, 2004

JOSÉ C. SEGURA LUNAJOSÉ C. SEGURA LUNA

GSTC UGRGSTC UGR