{'fraction_non_alphanumeric': 0.020114942528735632, 'fraction_numerical': 0.0021551724137931034, 'mean_word_length': 5.449074074074074, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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 tele-conference 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 Kaneda et al. Although the AMNOR has been proven effective, it can be further improved if we know spectrum characteristics of the desired distant signals in advance. Therefore, we regarded speech as a desired distant signal and designed an AMNOR based on the average speech spectrum. In this paper, we particularly focused on the performance of 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 confirmed that the ASR (automatic speech recognition) performance was improved 5-10% by using AMNOR based on the average speech spectrum in noisy environments. In addition, the proposed AMNOR provides better noise reduction performance than that of conventional AMNOR.