Towards spike-based speech processing: A biologically plausible approach to simple acoustic classification

• Autorzy: Ismail Uysal , Harsha Sathyendra , John G. Harris
• Języki publikacji: EN

Abstrakty

EN

Shortcomings of automatic speech recognition (ASR) applications are becoming more evident as they are more widely used in real life. The inherent non-stationarity associated with the timing of speech signals as well as the dynamical changes in the environment make the ensuing analysis and recognition extremely difficult. Researchers often turn to biology seeking clues to make better engineered systems, and ASR is no exception with the usage of feature sets such as Mel frequency cepstral coefficients, which employ filter banks similar to cochlear filter banks in frequency distribution and bandwidth. In this paper, we delve deeper into the mechanics of the human auditory system to take this biological inspiration to the next level. The main goal of this research is to investigate the computation potential of spike trains produced at the early stages of the auditory system for a simple acoustic classification task. First, various spike coding schemes from temporal to rate coding are explored, together with various spike-based encoders with various simplicity levels such as rank order coding and liquid state machine. Based on these findings, a biologically plausible system architecture is proposed for the recognition of phonetically simple acoustic signals which makes exclusive use of spikes for computation. The performance tests show superior performance on a noisy vowel data set when compared with a conventional ASR system.

Słowa kluczowe

EN

spike coding; synchrony coding; phase locking; speech perception; psychoacoustics; speech recognition

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2008
• Tom: 18
• Numer: 2
• Strony: 129-137

Daty

wydano

2008

otrzymano

2007-04-17

poprawiono

2007-09-06

Twórcy

autor

Ismail Uysal

• Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL 32611, USA

autor

Harsha Sathyendra

• Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL 32611, USA

autor

John G. Harris

• Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL 32611, USA

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Identyfikatory

DOI

10.2478/v10006-008-0012-0