對語音增強的單聲道噪音消除演算法

Abstract

This paper will propose an optimized speech enhancement algorithm aimed at single-channel noise reduction (NR) ,and apply the NR algorithm in the speech recognition. The optimization process is based on an objective function obtained in a regression model and the simulated annealing (SA) algorithm that is well suited for problems with many local optima. The NR algorithm, minimum mean-square error noise reduction (MMSE-NR) algorithm, employs a time-recursive averaging (TRA) method for noise estimation. Objective tests were undertaken to compare the optimized MMSE-TRA-NR and MMSE-VAD-TRA-NR algorithm with several conventional NR algorithms. White noise and car noise at signal-to-noise ratio (SNR) 5 dB are used in these tests. As compared to conventional algorithms, the optimized MMSE-TRA-NR and MMSE-VAD-TRA-NR algorithm proved effective in enhancing noise-corrupted speech signals, without compromising the timbral quality. The optimized MMSE-TRA-NR algorithm also can be used in automatic speech recognition (ASR), the recognition rate will be enhance by the optimal parameters of the MMSE-TRA-NR algorithms.

This paper will propose an optimized speech enhancement algorithm aimed at single-channel noise reduction (NR) ,and apply the NR algorithm in the speech recognition. The optimization process is based on an objective function obtained in a regression model and the simulated annealing (SA) algorithm that is well suited for problems with many local optima. The NR algorithm, minimum mean-square error noise reduction (MMSE-NR) algorithm, employs a time-recursive averaging (TRA) method for noise estimation. Objective tests were undertaken to compare the optimized MMSE-TRA-NR and MMSE-VAD-TRA-NR algorithm with several conventional NR algorithms. White noise and car noise at signal-to-noise ratio (SNR) 5 dB are used in these tests. As compared to conventional algorithms, the optimized MMSE-TRA-NR and MMSE-VAD-TRA-NR algorithm proved effective in enhancing noise-corrupted speech signals, without compromising the timbral quality. The optimized MMSE-TRA-NR algorithm also can be used in automatic speech recognition (ASR), the recognition rate will be enhance by the optimal parameters of the MMSE-TRA-NR algorithms.