{'fraction_non_alphanumeric': 0.02155576382380506, 'fraction_numerical': 0.0, 'mean_word_length': 5.068181818181818, '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}}
Microphone array sub-band speech recognition

Proposes the integration of sub-band speech recognition with a microphone array. A broadband beamforming microphone array allows for natural integration with sub-band speech recognition as the beamformer is typically implemented as a combination of band-limited sub-arrays. In the paper, rather than recombining the sub-array outputs to give a single enhanced output, we propose the fusion of separate hidden Markov models trained on each subarray frequency band. In addition, a dynamic sub-band weighting scheme is proposed in which the cross- and auto-spectral densities of the microphone array inputs are used to estimate the reliability of each frequency band. The microphone array sub-band system is evaluated on an isolated digit recognition task and compared to the standard full-band approach. The results of the proposed dynamic weighting scheme are compared to those obtained using both fixed equal sub-band weights, as well as optimal sub-band weights calculated from a priori knowledge of the correct results.