Noisy speech recognition using blind spatial subtraction array technique and deep bottleneck features
Norihide Kitaoka, Tomoki Hayashi, Kazuya Takeda · 2014
In this study, we investigate the effect of blind spatial subtraction arrays (BSSA) on speech recognition systems by comparing the performance of a method using Mel-Frequency Cepstral Coefficients (MFCCs) with a method using Deep Bottleneck Features (DBNF) based on Deep Neural Networks (DNN). Performance is evaluated under various conditions, including noisy, in-vehicle conditions. Although performance of the DBNF-based system was much more degraded by noise than the MFCC-based system, BSSA improved the performance of both methods greatly, especially when matched condition training of acoustic models was employed. These results show the effectiveness of BSSA for speech recognition.