JISE


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Journal of Information Science and Engineering, Vol. 22 No. 5, pp. 999-1013


MAP-Based Perceptual Modeling for Noisy Speech Recognition

Yung-Ji Sher1,3, Yeou-Jiunn Chen4, Yu-Hsien Chiu5 Kao-Chi Chung1 and Chung-Hsien Wu2
1Institute of Biomedical Engineering 
2Department of Computer Science and Information Engineering 
National Cheng Kung University 
Tainan, 701 Taiwan 
3Department of Physical Therapy 
Shu Zen College of Medicine and Management 
Kaohsiung, 821 Taiwan 
4Department of Electrical Engineering 
Southern Taiwan University of Technology 
Tainan, 710 Taiwan 
5Computer and Communications Research Laboratories 
Industrial Technology Research Institute 
Hsinchu, 310 Taiwan

    This study presents a maximum a posteriori (MAP) based perceptual modeling approach to deal with the issue of recognition degradation in noisy environment. In this approach, MAP-based noise detection is first applied to identify the noise segment in an utterance. Subtractive-type enhancement algorithm with masking properties of the human auditory system is then used to reduce the noise effect. Finally, MAP-based incremental noise model adaptation is developed to overcome the model inconsistencies between training and testing environments. For performance evaluation of the proposed approach, a Mandarin keyword recognition system was constructed. The experimental results show that the proposed approach achieves a better recognition rate compared to the audible noise suppression (ANS) and parallel model combination (PMC) methods.

Keywords: noisy speech recognition, speech enhancement, audible noise suppression, MAP-based perceptual modeling, noise detection, incremental model adaptation

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