Suitable design of adaptive beamformer based on average speech spectrum for noisy speech recognition

Abstract

Recognition of distant-talking speech is indispensable for self-moving robots or teleconference systems. However, background noise and room reverberations seriously degrade the sound capture quality in real acoustic environments. A microphone array is an ideal candidate as an effective method for capturing distant-talking speech. AMNOR (Adaptive Microphone-array for NOise Reduction) was proposed an adaptive beamformer for capturing the desired distant signals in noisy environments by Kaneda et al. Although AMNOR has proven itself effective, it could be further improved if we knew the spectrum characteristics of desired distant signals in advance. Therefore, in this paper we regard speech as a desired distant signal and design AMNOR based on the average speech spectrum for distant-talking speech capture and recognition. As a result of evaluation experiments in real acoustic environments, we could confirm that the ASR (Automatic Speech Recognition) performance was improved 5 ~ 10% by AMNOR based on average speech spectrum in noisy environments.