Speech Detection in Non-Stationary Noise Based on the 1/f Process

In this paper,an effective and robust active speech detection method is proposed based on the 1/f process technique for signals under non-stationary noisy environments.The Gaussian 1/f process ,a mathematical model for statistically self-similar radom processes based on fractals,is selected to model the speech and the background noise.An optimal Bayesian two-class classifier is developed to discriminate them by their 1/f wavelet coefficients with Karhunen-Loeve-type properties.Multiple templates are trained for the speech signal,and the parameters of the background noise can be dynamically adapted in runtime to model the variation of both the speech and the noise.In our experiments,a 10-minute long speech with different types of noises ranging from 20dB to 5dB is tested using this new detection method.A high performance with over 90% detection accuracy is achieved when average SNR is about 10dB.

Speech detection in non-stationary noise based on the 1/f process

In this paper, an effective and robust active speech detection method is proposed based on the 1/f process technique for signals under non-stationary noisy environments. The Gaussian 1/f process, a mathematical model for statistically self-similar random processes based on fractals, is selected to model both the speech and the background noise. An optimal Bayesian two-class classifier is developed to discriminate them by their 1/f wavelet coefficients with Karhunen-Loeve-type properties. Multiple templates are trained for the speech signal, and the parameters of the background noise can be dynamically adapted in runtime to model the variation of both the speech and the noise. In our experiments, a 10-minute long speech with different types of noises ranging from 20dB to 5dB is tested using this new detection method. A high performance with over 90% detection accuracy is achieved when average SNR is about 10dB. WANG Fan was born in 1974. He received his B.S. degree in computer science and technology from the Department of Computer Science and technology, Tsinghua University in 1998. He is currently a Ph.D. candidate and research assistant, majoring in computer applications. His current research interests focus on robust speech recognition and understanding. In 2000, he received the Excellent Student Paper Award in the ’2000 International Symposium on Chinese Spoken Language Processing (ISCSLP’2000). He is an ACM member and the chair of Tsinghua ACM Student Chapter. ZHENG Fang is currently an associate professor of Tsinghua University. He is director of the Center of Speech Technology, State Key Laboratory of Intelligent Technology and Systems. He received his B.S., M.S. and Ph.D. degrees in computer science and technology from Tsinghua University, in 1990, 1992 and 1997 respectively. He has been working on speech recognition and understanding at the Department of Computer science and Technology, Tsinghua University, since 1988. He has published over 80 technical papers on acoustic/language modeling, isolated/continuous speech recognition, keyword spotting, dictating, language understanding and so on. He is an IEEE member and a member of the Editorial Committee of the Journal of Chinese Information Processing. WU Wenhu received his B.S. degree in automation in 1961 from Tsinghua University. Since then, he has been with Tsinghua University, where he is currently a full professor in the Department of Computer Science and Technology. His major research interests include speech recognition and language understanding, speech synthesis, digital processing of speech signals, and so on. As a principal or key undertaker, he has been taking part in many state important tasks and the ‘863’ Hi-Tech projects and has been awarded several times.