{'fraction_non_alphanumeric': 0.021632251720747297, 'fraction_numerical': 0.0, 'mean_word_length': 5.567741935483871, '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}}
Bayesian model based non-intrusive speech quality evaluation

A novel Bayesian model-based non-intrusive speech quality evaluation (BM-NiSQE) algorithm is presented in this paper. The proposed BM-NiSQE algorithm employs a statistical model approach and Bayesian inference to estimate the speech quality only using the output signal of the system under test. In the proposed algorithm, the speech features are extracted by perceptual spectral analysis. Gaussian mixture density hidden Markov models (GMD-HMMs) are exploited to characterize different speech quality categories, which take into account not only the temporal variations of speech signal but also the spectral statistical characteristics in the perception domain. Based on the trained GMD-HMMs, the prediction of speech quality is carried out by Bayesian inference and minimum mean square error (MMSE) estimation. Preliminary experimental results show that the predicted results of the proposed algorithm correlate well with the subjective quality scores.