EVERA: An Evolutionary Programming Environment for Adaptive Speech Processing

Abstract In this paper, we describe EVERA (Environnement de Vie artificielle pour l'Etude de la Robustesse des Apprentis), a system for the modeling and simulation of adaptive speech processing systems. The possibility to combine genetic algorithms and artificial neural networks (ANNs) in the EVERA system allows the study of “evolutive” speech recognition systems. EVERA is written in an object-oriented framework and provides a high-level graphic user interface to design, observe and to monitor different simulations. We present some applications of the system to the problem of robust speech perception and recognition in hostile synthetic acoustic environments built from some physical model of real rooms (4 walls, 1 floor and 1 ceiling) which include different real world noises.

[1]  B. Stein,et al.  The Merging of the Senses , 1993 .

[2]  F. Varela.  Principles of biological autonomy , 1979 .

[3]  Richard K. Belew,et al.  Artificial life: a constructive lower bound for artificial intelligence , 1991, IEEE Expert.

[4]  Alan C. Bovik,et al.  Computer lipreading for improved accuracy in automatic speech recognition , 1996, IEEE Trans. Speech Audio Process..

[5]  Biing-Hwang Juang,et al.  A family of distortion measures base upon projection operation for robust speech recognition , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[6]  H Hermansky,et al.  Perceptual linear predictive (PLP) analysis of speech. , 1990, The Journal of the Acoustical Society of America.

[7]  Bruce J. MacLennan,et al.  Synthetic Ethology and the Evolution of Cooperative Communication , 1993, Adapt. Behav..

[8]  Andrew Varga,et al.  Control experiments on noise compensation in hidden Markov model based continuous word recognition , 1989, EUROSPEECH.

[9]  H. Kabre,et al.  On the active perception of speech by robots , 1996, 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242).

[10]  Rodney A. Brooks,et al.  Artificial Life and Real Robots , 1992 .

[11]  Harouna Kabre.  Performance and competence models for audiovisual data fusion , 1995, Other Conferences.

[12]  Harouna Kabre,et al.  Speech recognition in the real world: artificial neural networks and robustness , 1997, Defense, Security, and Sensing.

[13]  Thomas S. Ray,et al.  Is It Alive or Is It GA? , 1991, ICGA.

[14]  David H. Ackley,et al.  Interactions between learning and evolution , 1991 .

[15]  Q. Summerfield.  Audio-visual Speech Perception, Lipreading and Artificial Stimulation , 1983 .

[16]  D. C. Bateman,et al.  Spectral contrast normalization and other techniques for speech recognition in noise , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[18]  Jont B. Allen,et al.  Image method for efficiently simulating small‐room acoustics , 1976 .

[19]  Charles E. Taylor,et al.  Artificial Life II , 1991 .

[20]  Michael Picheny,et al.  Adaptation techniques for ambience and microphone compensation in the IBM Tangora speech recognition system , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[21]  Yifan Gong,et al.  Speech recognition in noisy environments: A survey , 1995, Speech Commun..

[22]  T. Michael Knasel,et al.  Robotics and autonomous systems , 1988, Robotics Auton. Syst..

[23]  Stewart W. Wilson,et al.  Environment structure and adaptive behavior from the ground up , 1993 .

[24]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[25]  Q. Summerfield.  Some preliminaries to a comprehensive account of audio-visual speech perception. , 1987 .

[26]  R. A. Brooks,et al.  Intelligence without Representation , 1991, Artif. Intell..

[27]  Eric D. Petajan.  Automatic lipreading to enhance speech recognition , 1984 .

[28]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .