{'fraction_non_alphanumeric': 0.02274641954507161, 'fraction_numerical': 0.0016849199663016006, 'mean_word_length': 5.947368421052632, '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}}
Effects of noise spectrum estimation algorithms on speech intelligibility

Noise spectrum estimation is a key step in single channel speech enhancement algorithms. Most of current speech enhancement algorithms are designed to improve speech quality, however, algorithms for increasing speech intelligibility are few. The traditional speech enhancement algorithms improve speech quality, while sacrificing speech intelligibility. In this paper, classical noise spectrum estimation algorithms are evaluated for their effects on speech intelligibility. Noise spectrum is estimated in different noise environments with SNRs between ?9 d B and 3 d B. The spectral subtraction is thereafter used for speech denoising. The STOI(Short-Time Objective Intelligibility) value of the enhanced speech is computed. At last, according to the signal-to-noise ratio, the proportions of speech dominated time-frequency blocks under different noise environments are analyzed. Experimental results show that, compared with other noise estimation algorithms, the minimum statistics(MS) obtains high speech intelligibility because it retains more speech dominated time-frequency blocks after speech denoising.