SNR-Based Mask Compensation for Computational Auditory Scene Analysis Applied to Speech Recognition in a Car Environment

In this paper, we propose a computational auditory scene analysis (CASA)-based front-end for two microphone speech recognition in a car environment. One of the important issues associated with CASA is the accurate estimation of mask information for target speech separation within multiple microphone noisy speech. For such a task, the time frequency mask information is compensated through the signal to noise ratio resulted from a beamformer to adjust the noise quantity included in noisy speech. We evaluate the performance of an automatic speech recognition (ASR) system employing a CASA-based front-end with the proposed mask compensation method. In addition, we compare its performance with those employing a CASA-based front-end without mask compensation and the beamforming based front-end. As a result, the CASA-based front-end achieves an average word error rate (WER) reduction of 8.57% when the proposed mask compensation method is applied. In addition, the CASA-based front-end with the proposed method provides a relative WER reduction of 26.52%, compared with the beamforming-based front-end.