In this paper, we propose a method to compensate for noise and speaker-variability directly in the Log filter-bank (FB) domain, so that MFCC features are robust to noise and speaker-variations. For noise-compensation, we use Vector Taylor Series (VTS) approach in the Log FB domain, and speaker-normalization is also done in the Log FB domain using Linear Vocal tract length (VTLN) matrices. For VTLN, optimal selection of warp-factor is done in Log FB domain using canonical GMM model, avoiding the two-pass approach needed by a HMM model. Further, this can be efficiently implemented using sufficient statistics obtained from the GMM and the FB-VTLN-matrices. The warp-factor selection using GMM can also be done in cepstral domain by applying DCT matrices without the usual approximations associated with conventional linear-VTLN. The elegance of the proposed approach is that given the speech data, we obtain directly MFCC features that are robust to noise and speaker-variations. The proposed approach, show a significant relative improvement of 31% over baseline on Aurora-4 task. © 2012 IEEE.

Srinivasan Umesh

Department of Electrical Engineering

Raghavendra R. Bilgi

CEPSTRAL DOMAIN

HMM MODELS

NOISE COMPENSATION

OPTIMAL SELECTION

SPEAKER NORMALIZATION

speech data

SUFFICIENT STATISTICS

TVTLN

VECTOR TAYLOR SERIES

VOCAL TRACT LENGTHS

Filter banks

Matrix algebra

Speech Recognition

Acoustic noise

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Noise and speaker compensation in the Log filter bank domain

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

This paper describes a novel framework to sub-band based Histogram Equalization (HEQ) applied to robust speech recognition. We propose a frequency band specific equalization to compensate the noise distortion on the individual frequency bands. The proposed equalization framework is a two step process. In the first step, conventional histogram equalization is done. By analyzing the histograms of equalized cepstra, we show that the first stage of conventional HEQ approach does not compensate the sub-band specific noise distortion, even though the overall histogram is normalized. Hence, in the second stage, sub-band specific histogram equalization is done. Every frame of cepstral coefficients is decomposed into low-frequency (LF) cepstra and high-frequency (HF) cepstra. Separate equalization is done on LF and HF cepstra to compensate LF and HF specific noise distortion. The cepstra corresponding to the LF and HF bands are obtained by using simple averaging and differencing filters on the cepstral components within a particular frame. The proposed approach is referred to as Sub-band Histogram Equalization (S-HEQ). Using histogram analysis, we show that the S-HEQ approach is able to compensate for the sub-band specific noise distortion. S-HEQ approach shows a consistent improvement over the conventional HEQ approach with a relative improvement of 12% and 22.10% over conventional HEQ in WER on Aurora-2 and Aurora-4 databases respectively. Proposed equalization approach can also be used with the deep neural network based systems and has shown a consistent improvement in the recognition accuracies over conventional HEQ. Finally, the efficacy of the proposed S-HEQ approach for embedded real-time speech applications is shown by comparing the performance and computational complexity trade-off with other state-of-the-art noise compensation methods. © 2015 Elsevier B.V. All rights reserved.

Speech Communication

Sub-band based histogram equalization in cepstral domain for speech recognition

This work deals with strategies to jointly reduce the speaker and environment mismatches in Automatic Speech Recognition. The consequences of environmental mismatch in the performance of conventional Vocal Tract Length Normalization algorithm are analyzed, observing the sensitivity of the warping factor distributions to the SNR fall. A new combined speaker-noise normalization strategy which reduces the effect of noise in VTLN by applying Histogram Equalization is proposed and experimented in AURORA2 and AURORA4 databases. Solid results are obtained and discussed to analyze the effectiveness of the described technique. © 2011 IEEE.

Combining speaker and noise feature normalization techniques for automatic speech recognition

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Rapid joint speaker and noise compensation for robust speech recognition

Computer Speech & Language

Frequency warping for VTLN and speaker adaptation by linear transformation of standard MFCC

IEEE Transactions on Speech and Audio Processing

A frequency warping approach to speaker normalization

Noise and speaker compensation in the log filter bank domain

Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN	15206149
Open Access	No