Fuzzy Logic Speech/Non-speech Discrimination for Noise Robust Speech Processing

Abstract

Abstract. This paper shows a fuzzy logic speech/non-speech discrimination method for improving the performance of speech processing systems working in noise environments. The fuzzy system is based on a Sugeno inference engine with membership functions defined as combination of two Gaussian functions. The rule base consists of ten fuzzy if then statements defined in terms of the denoised subband signal-tonoise ratios (SNRs) and the zero crossing rates (ZCRs). Its operation is optimized by means of a hybrid training algorithm combining the leastsquares method and the backpropagation gradient descent method for training membership function parameters. The experiments conducted on the Spanish SpeechDat-Car database shows that the proposed method yields clear improvements over a set of standardized VADs for discontinuous transmission (DTX) and distributed speech recognition (DSR) and also over recently published VAD methods.

Development of a Voice Activity Controlled Noise Canceller

“…Voice activity detection techniques relying on artificial intelligence and soft computing have emerged in recent years to surmount the problem of VAD. These techniques include the use of support vector machine [ 28 ], neural networks [ 29 ], and fuzzy logic [ 30 ]. These classification strategies practically fail to solve the problem due to the non-stationary nature of both the speech and the background noise.…”