Application of HMM Adaptation and Robust Features to Sub-Band Speech Recognition in Noise
Publish place: 11th Annual Conference of Computer Society of Iran
Publish Year: 1384
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ACCSI11_211
تاریخ نمایه سازی: 5 آذر 1390
Abstract:
In recent years, sub-band speech recognition has been found useful in robust speech recognition, especially for speech signals contaminated by band-limited noise. In sub-band speech recognition, full band speech is divided into several frequency sub-bands. Sub-band feature vectors or their generated likelihoods by corresponding sub-band recognizers are combined to give the result of recognition task. In this paper, we concatenate sub-band feature vectors, where we extract phase autocorrelation (PAC) MFCC and one type of group delay based MFCC, called MFPSCC, as noise robust features from each subband. Furthermore, we used a model adaptation method, named weighted projection measure (WPM), to adapt HMM Gaussian mean vectors to concatenated sub-band feature vectors in noisy conditions. The experimental results indicate that the proposed methods significantly improve the sub-band speech recognition system performance in presence of additive noise
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Authors
Babak Nasersharif
Iran University of Science & Technology
Mohammad Mehdi Homayounpour
AmirKabir University of Technology
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