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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 A Comprehensive Noise Robust Speech Parameterization Algorithm Using Wavelet Packet | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 64102 20 pages doi 10.1155 2007 64102 Research Article A Comprehensive Noise Robust Speech Parameterization Algorithm Using Wavelet Packet Decomposition-Based Denoising and Speech Feature Representation Techniques Bojan Kotnikand Zdravko Kacic Faculty of Electrical Engineering and Computer Science University ofMaribor Smetanova ul. 17 2000 Maribor Slovenia Received 22 May 2006 Revised 12 January 2007 Accepted 11 April 2007 Recommended by Matti Karjalainen This paper concerns the problem of automatic speech recognition in noise-intense and adverse environments. The main goal of the proposed work is the definition implementation and evaluation of a novel noise robust speech signal parameterization algorithm. The proposed procedure is based on time-frequency speech signal representation using wavelet packet decomposition. A new modified soft thresholding algorithm based on time-frequency adaptive threshold determination was developed to efficiently reduce the level of additive noise in the input noisy speech signal. A two-stage Gaussian mixture model GMM -based classifier was developed to perform speech nonspeech as well as voiced unvoiced classification. The adaptive topology of the wavelet packet decomposition tree based on voiced unvoiced detection was introduced to separately analyze voiced and unvoiced segments of the speech signal. The main feature vector consists of a combination of log-root compressed wavelet packet parameters and autoregressive parameters. The final output feature vector is produced using a two-staged feature vector postprocessing procedure. In the experimental framework the noisy speech databases Aurora 2 and Aurora 3 were applied together with corresponding standardized acoustical model training testing procedures. The automatic speech recognition performance achieved using the proposed noise robust speech parameterization procedure was .
