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 Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 878105 15 pages doi 2009 878105 Research Article Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition Bagher BabaAli Hossein Sameti and Mehran Safayani Department of Computer Engineering Sharif University of Technology Tehran Iran Correspondence should be addressed to Bagher BabaAli babaali@ Received 12 May 2008 Revised 17 December 2008 Accepted 19 January 2009 Recommended by D. O Shaughnessy Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction SS is a well-known and effective approach it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless correlation can be expected between speech quality improvement and the increase in recognition accuracy. This paper proposes a novel approach for solving this problem by considering SS and the speech recognizer not as two independent entities cascaded together but rather as two interconnected components of a single system sharing the common goal of improved speech recognition accuracy. This will incorporate important information of the statistical models of the recognition engine as a feedback for tuning SS parameters. By using this architecture we overcome the drawbacks of previously proposed methods and achieve better recognition accuracy. Experimental evaluations show that the proposed method can achieve significant improvement of recognition rates across a wide .
