{'fraction_non_alphanumeric': 0.023542600896860985, 'fraction_numerical': 0.002242152466367713, 'mean_word_length': 5.471014492753623, '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}}
Robust automatic speech recognition using a multi-channel signal separation front-end

A multi-channel signal separation front-end for robust automatic speech recognition under time-varying interference conditions is developed. The speech signals acquired by a dual-channel system are restored by adaptive decorrelation filtering, and then examined by a time-domain or frequency-domain source signal detection technique to determine the active regions of each source signal. The front-end is integrated with an HMM-based speaker-independent continuous speech recognition system by providing the restored signals within the active regions for recognition. Under a simulated room acoustic condition, the overall system shows very promising performance. For the conditions with SNR above -10 dB, the achieved word recognition accuracies are very close to that of the interference-free condition.