An Overview of Technical Progress in Speech Recognition

This paper presents a brief survey on Speech Recognition and discusses the major themes and advances made in the past few 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, and database and performance evaluation. The problems that are existing in SR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. 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.

[1]  Kohji Fukunaga,et al.  Introduction to Statistical Pattern Recognition-Second Edition , 1990 .

[2]  T. B. Martin,et al.  SPEECH RECOGNITION BY FEATURE-ABSTRACTION TECHNIQUES. , 1964 .

[3]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[4]  Frederick Jelinek,et al.  The development of an experimental discrete dictation recognizer , 1985, Proceedings of the IEEE.

[5]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[6]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[7]  Lawrence R. Rabiner,et al.  A tutorial on Hidden Markov Models , 1986 .

[8]  Rabab Kreidieh Ward,et al.  Vector Quantization Technique for Nonparametric Classifier Design , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  S. Klinke,et al.  Exploratory Projection Pursuit , 1995 .

[10]  Harry F. Olson,et al.  Phonetic typewriter , 1957 .

[11]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[12]  José L. Pérez-Córdoba,et al.  Histogram equalization of speech representation for robust speech recognition , 2005, IEEE Transactions on Speech and Audio Processing.

[13]  Lalit R. Bahl,et al.  Design of a linguistic statistical decoder for the recognition of continuous speech , 1975, IEEE Trans. Inf. Theory.

[14]  R. Gray,et al.  Combining Image Compression and Classification Using Vector Quantization , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[16]  K. Nagata.  Spoken digit recognizer for Japanese language. , 1963 .

[17]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  J. Forgie,et al.  Results Obtained from a Vowel Recognition Computer Program , 1959 .

[19]  K. Davis,et al.  Automatic Recognition of Spoken Digits , 1952 .

[20]  Shuji Doshita,et al.  The Phonetic Typewriter , 1962, IFIP Congress.

[21]  Andreas Stolcke,et al.  Enriching speech recognition with automatic detection of sentence boundaries and disfluencies , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[22]  D. R. Reddy.  An approach to computer speech recognition by direct analysis of the speech wave , 1966 .

[23]  C. D. Forgie,et al.  Automatic Recognition of Spoken Digits , 1958 .

[24]  T. K. Vintsyuk.  Speech discrimination by dynamic programming , 1968 .

[25]  N. G. Zagoruyko,et al.  Automatic recognition of 200 words , 1970 .