Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 32546 7 pages doi 2007 32546 Research Article Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition Geng-Xin Ning Gang Wei and Kam-Keung Chu School of Electronic and Information Engineering South China University of Technology Guangzhou 510640 China Received 8 October 2005 Revised 20 December 2006 Accepted 20 December 2006 Recommended by Douglas O Shaughnessy This paper presents a novel model compensation MC method for the features of mel-frequency cepstral coefficients MFCCs with signal-to-noise-ratio- SNR- dependent nonuniform spectral compression SNSC . Though these new MFCCs derived from a SNSC scheme have been shown to be robust features under matched case they suffer from serious mismatch when the reference models are trained at different SNRs and in different environments. To solve this drawback a compressed mismatch function is defined for the static observations with nonuniform spectral compression. The means and variances of the static features with spectral compression are derived according to this mismatch function. Experimental results show that the proposed method is able to provide recognition accuracy better than conventional MC methods when using uncompressed features especially at very low SNR under different noises. Moreover the new compensation method has a computational complexity slightly above that of conventional MC methods. Copyright 2007 Geng-Xin Ning et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION The problem of achieving robust speech recognition in noisy environments has aroused much interest in the past decades. However drastic degradation of performance may still occur when a recognizer .
