Internal noise suppression for speech recognition by small robots

2005 ◽  
Author(s):  
Akinori Ito ◽  
Takashi Kanayama ◽  
Motoyuki Suzuki ◽  
Shozo Makino
Keyword(s):  
Speech Recognition ◽  
Noise Suppression ◽  
Internal Noise

scholarly journals Direct Recovery of Clean Speech Using a Hybrid Noise Suppression Algorithm for Robust Speech Recognition System

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Peng Dai ◽  
Ing Yann Soon ◽  
Rui Tao
Keyword(s):  
Speech Recognition ◽  
Noise Suppression ◽  
Recognition Rate ◽  
Nonlinear Function ◽  
Recognition System ◽  
Speech Recognition System ◽  
Direct Solution ◽  
Power Domain ◽  
Discontinuity Problem ◽  
Mel Frequency Cepstral Coefficient

A new log-power domain feature enhancement algorithm named NLPS is developed. It consists of two parts, direct solution of nonlinear system model and log-power subtraction. In contrast to other methods, the proposed algorithm does not need prior speech/noise statistical model. Instead, it works by direct solution of the nonlinear function derived from the speech recognition system. Separate steps are utilized to refine the accuracy of estimated cepstrum by log-power subtraction, which is the second part of the proposed algorithm. The proposed algorithm manages to solve the speech probability distribution function (PDF) discontinuity problem caused by traditional spectral subtraction series algorithms. The effectiveness of the proposed filter is extensively compared using the standard database, AURORA2. The results show that significant improvement can be achieved by incorporating the proposed algorithm. The proposed algorithm reaches a recognition rate of over 86% for noisy speech (average from SNR 0 dB to 20 dB), which means a 48% error reduction over the baseline Mel-frequency Cepstral Coefficient (MFCC) system.

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