Speech Recognition by Machine, A Review

This paper presents a brief survey on Automatic Speech Recognition and discusses the major themes and advances made in the past 60 years of research, so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in this important area of speech communication. After years of research and development the accuracy of automatic speech recognition remains one of the important research challenges (e.g., variations of the context, speakers, and environment).The design of Speech Recognition system requires careful attentions to the following issues: Definition of various types of speech classes, speech representation, feature extraction techniques, speech classifiers, database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. Hence authors hope that this work shall be a contribution in the area of speech recognition. The objective of this review paper is to summarize and compare some of the well known methods used in various stages of speech recognition system and identify research topic and applications which are at the forefront of this exciting and challenging field.Comment: 25 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS December 2009, ISSN 1947 5500, http://sites.google.com/site/ijcsis

Robust speech feature extraction by growth transformation in reproducing kernel Hilbert space

Abstract—The performance of speech recognition systems depends on consistent quality of the speech features across variable environmental conditions encountered during training and evaluation. This paper presents a kernel-based nonlinear predictive coding procedure that yields speech features which are robust to nonstationary noise contaminating the speech signal. Features maximally insensitive to additive noise are obtained by growth transformation of regression functions that span a reproducing kernel Hilbert space (RKHS). The features are normalized by construction and extract information pertaining to higher-order statistical correlations in the speech signal. Experiments with the TI-DIGIT database demonstrate consistent robustness to noise of varying statistics, yielding significant improvements in digit recognition accuracy over identical models trained using Mel-scale cepstral features and evaluated at noise levels between 0 and 30-dB signal-to-noise ratio. Index Terms—Feature extraction, growth transforms, noise robustness, nonlinear signal processing, reproducing kernel Hilbert Space, speaker verification. I

Robust Speech Feature Extraction by Growth Transformation in Reproducing Kernel Hilbert Space

A robust speech feature extraction procedure, by kernel regression nonlinear predictive coding, is presented. Features maximally insensitive to additive noise are obtained by growth transformation of regression functions spanning a Reproducing Kernel Hilbert Space (RKHS). Experiments on TI-DIGIT demonstrate consistent robustness of the new features to noise of varying statistics, yielding significant improvements in digit recognition accuracy over identical models trained using Mel-scale cepstral features and evaluated at noise levels between 0 and 30 dB SNR. 1