An efficient noise reduction algorithm is proposed to improve speech recognition performance for human machine interfaces. In the algorithm, a probabilistic adaptation mode controller (AMC) is designed and adopted to the generalized sidelobe canceller (GSC). To detect target speech intervals, the proposed AMC calculates the inter-channel correlation and estimates speech absence probability (SAP). Based on the SAP, the adaptation mode of the adaptive filter in the GSC is decided. Experimental results show the proposed algorithm significantly improves speech recognition performances and signal-to-noise ratios in real noisy environments.

This letter mainly focuses on improving current noise reduction methods to solve the critical speech distortion problems with robust noise reduction in noisy speech signals for speech enhancement over IP networks. For robust noise reduction with packet loss recovery, we propose a novel optimized Wiener filtering technique that uses the estimated SNR (Signal-to-Noise Ratio) with packet loss recovery method which is applied as post-filtering over IP-networks. Simulation results demonstrate that the proposed scheme provides better reduction and recovery rates with considering packet loss and SNR environment than other methods.