A SELF-ADAPTING ENDPOINT DETECTION ALGORITHM FOR SPEECH RECOGNITION IN NOISYENVIRONMENTS BASED ON 1/F PROCESS
Fan Wang, Wenhu Wu · 2000
This paper presents an effective and robust speech endpoint detection method based on 1/f process technique, which is suitable for robust continuous speech recognition system in variable noisy environments. The Gaussian 1/f process, which is a mathematical model for statistically self-similar random processes from fractals, is selected to model both speech and background noise. Then, an optimal Bayesian two-class classifier is developed to discriminate between real noisy speech and background noise by the wavelet coefficients with Karhunen-Loeve-type properties of the 1/f processes. Finally, for robust requirement, a few templates are built for speech and the parameters of the background noise can be dynamically adapted in runtime to deal with the variation of both speech and noise. In our experiments, 10 minutes long speech with different types of noises was tested using this new endpoint detector. A high performance with over 90 % detection accuracy was achieved. 1.