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báo cáo hóa học:" A novel voice activity detection based on phoneme recognition using statistical model"

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: A novel voice activity detection based on phoneme recognition using statistical model | EURASIP Journal on Audio Speech and Music Processing SpringerOpen0 This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text HTML versions will be made available soon. A novel voice activity detection based on phoneme recognition using statistical model EURASIP Journal on Audio Speech and Music Processing 2012 2012 1 doi 10.1186 1687-4722-2012-1 Xulei Bao qunzhong@sjtu.edu.cn Jie Zhu zhujie@sjtu.edu.cn ISSN 1687-4722 Article type Research Submission date 19 September 2011 Acceptance date 9 January 2012 Publication date 9 January 2012 Article URL http asmp.eurasipiournals.com content 2012 1 1 This peer-reviewed article was published immediately upon acceptance. It can be downloaded printed and distributed freely for any purposes see copyright notice below . For information about publishing your research in EURASIP ASMP go to http asmp.eurasipjournals.com authors instructions For information about other SpringerOpen publications go to http www.springeropen.com 2012 Bao and Zhu licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. A novel voice activity detection based on phoneme recognition using statistical model Xulei Bao and Jie Zhu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China Corresponding author qunzhong@sjtu.edu.cn Email address JZ zhujie@sjtu.edu.cn Email Corresponding author Abstract In this article a novel voice activity detection VAD approach based on phoneme recognition using Gaussian Mixture Model based Hidden Markov Model HMM GMM is proposed. Some sophisticated speech features such as high order statistics HOS harmonic structure information and Mel-frequency cepstral coefficients MFCCs are employed to represent each speech .