{'fraction_non_alphanumeric': 0.02875399361022364, 'fraction_numerical': 0.006389776357827476, 'mean_word_length': 5.438356164383562, '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}}
A Two-Channel Minimum Mean-Square Error Log-Spectral Amplitude Estimator for Speech Enhancement

This paper proposes a novel two-channel speech enhancement structure using the minimum mean-square error log- spectral amplitude (MMSE-LSA) estimator. The proposed two-channel enhancement algorithm utilizes a spatial relationship between two input signals to accurately estimate the noise power spectral density (PSD) needed for the MMSE-LSA algorithm. The proposed structure improves the noise reduction capacity with less speech distortion, while its complexity is much lower than simple cascade structures. The performance of the proposed algorithm is evaluated by automatic speech recognition tests in a car environment. Comparing to a simple cascading of two- and single-channel algorithms, the proposed algorithm improves the relative recognition rate by 17.5% for high speed conditions and 14.8% for low speed conditions, respectively.