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.