26-09-2016, 09:39 AM
26-09-2016, 03:47 PM
Abstract
In this paper, we propose a new speech enhancement system using the wavelet thresholding algorithm. The basic wavelet thresholding algorithm has some defects including the assumption of white Gaussian noise (WGN), malfunction in unvoiced segments, bad auditory quality, etc. In the proposed system, we introduce a new algorithm which does not require any voiced/unvoiced detection system. Also, in this proposed method adaptive wavelet thresholding and modified thresholding functions are introduced to improve the speech enhancement performance as well as the automatic speech recognition (ASR) accuracy. A new voice activity detector (VAD) was designed to update noise statistics in the proposed speech enhancement system when facing to the colored and non-stationary noises. The proposed method was evaluated on several speakers and under various noise conditions including white Gaussian noise, pink noise, and multi-talker babble noise. The SNR and ASR results show that the new method highly improves the performance of speech enhancement algorithm based on the wavelet thresholding.
In this paper, we propose a new speech enhancement system using the wavelet thresholding algorithm. The basic wavelet thresholding algorithm has some defects including the assumption of white Gaussian noise (WGN), malfunction in unvoiced segments, bad auditory quality, etc. In the proposed system, we introduce a new algorithm which does not require any voiced/unvoiced detection system. Also, in this proposed method adaptive wavelet thresholding and modified thresholding functions are introduced to improve the speech enhancement performance as well as the automatic speech recognition (ASR) accuracy. A new voice activity detector (VAD) was designed to update noise statistics in the proposed speech enhancement system when facing to the colored and non-stationary noises. The proposed method was evaluated on several speakers and under various noise conditions including white Gaussian noise, pink noise, and multi-talker babble noise. The SNR and ASR results show that the new method highly improves the performance of speech enhancement algorithm based on the wavelet thresholding.