An evaluation of adaptive beamformer based on average speech spectrum for noisy speech recognition

Distant-talking speech recognition in noisy environments 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 as an adaptive beamformer for capturing the desired distant signals in noisy environments by Y. Kaneda and J. Ohga (see IEEE Trans. Acoust. Speech Sig. Process., vol.ASSP-34, no.6, p.1391-1400, 1986). Although the AMNOR has been proven effective, it can be further improved if we know the spectrum characteristics of the desired distant signals in advance. Regarding speech as a desired distant signal, we have designed an AMNOR based on the average speech spectrum. We particularly focus on the performance of the proposed AMNOR for distant-talking speech capture and recognition. Evaluation experiments in real acoustic environments confirm that ASR (automatic speech recognition) performance in noisy environments is improved by 5-10% using our AMNOR. In addition, the proposed AMNOR provides better noise reduction performance than that of the conventional AMNOR.