tailieunhanh - Báo cáo hóa học: " Using Mel-Frequency Cepstral Coefficients in Missing Data Technique"

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: Using Mel-Frequency Cepstral Coefficients in Missing Data Technique | EURASIP Journal on Applied Signal Processing 2004 3 340-346 2004 Hindawi Publishing Corporation Using Mel-Frequency Cepstral Coefficients in Missing Data Technique Zhang Jun Department of Computer Science City University of Hong Kong Kowloon Hong Kong China School of Electronic and Communication Engineering South China University of Technology Guangzhou 510640 China Email zhpangun@ Sam Kwong Department of Computer Science City University of Hong Kong Kowloon Hong Kong China Email cssamk@ Wei Gang School of Electronic and Communication Engineering South China University of Technology Guangzhou 510640 China Email ecgwei@ Qingyang Hong Department of Computer Science City University of Hong Kong Kowloon Hong Kong China Email qyhong@ Received 19 February 2003 Revised 16 June 2003 Recommended for Publication by Mukund Padmanabhan Filter bank is the most common feature being employed in the research of the marginalisation approaches for robust speech recognition due to its simplicity in detecting the unreliable data in the frequency domain. In this paper we propose a hybrid approach based on the marginalisation and the soft decision techniques that make use of the Mel-frequency cepstral coefficients MFCCs instead of filter bank coefficients. A new technique for estimating the reliability of each cepstral component is also presented. Experimental results show the effectiveness of the proposed approaches. Keywords and phrases MFCC missing data techniques robust speech recognition. 1. INTRODUCTION In spite of many years of efforts the robustness of speech recognition in the noisy environment is still a fundamental unsolved issue in today s automatic speech recognition ASR systems. Recently missing data theory 1 2 3 4 is proposed as an operationalization to improve the robustness of the ASR decoding process. Experimental results show that it can significantly restore the ASR performance with little prior assumptions made about