Articulation entropy: An unsupervised measure of articulatory precision

Articulatory precision is a critical factor that influences speaker intelligibility. In this paper, we propose a new measure we call ‘articulation entropy’ that serves as a proxy for the number of distinct phonemes a person produces when he or she speaks. The method is based on the observation that the ability of a speaker to achieve an articulatory target, and hence clearly produce distinct phonemes, is related to the variation of the distribution of speech features that capture articulation - the larger the variation, the larger the number of distinct phonemes produced. In contrast to previous work, the proposed method is completely unsupervised, does not require phonetic segmentation or formant estimation, and can be estimated directly from continuous speech. We evaluate the performance of this measure with several experiments on two data sets: a database of English speakers with various neurological disorders and a database of Mandarin speakers with Parkinson’s disease. The results reveal that our measure correlates with subjective evaluation of articulatory precision and reveals differences between healthy individuals and individuals with neurological impairment

Speech enhancement Algorithm based on super-Gaussian modeling and orthogonal polynomials

© 2020 Lippincott Williams and Wilkins. All rights reserved. Different types of noise from the surrounding always interfere with speech and produce annoying signals for the human auditory system. To exchange speech information in a noisy environment, speech quality and intelligibility must be maintained, which is a challenging task. In most speech enhancement algorithms, the speech signal is characterized by Gaussian or super-Gaussian models, and noise is characterized by a Gaussian prior. However, these assumptions do not always hold in real-life situations, thereby negatively affecting the estimation, and eventually, the performance of the enhancement algorithm. Accordingly, this paper focuses on deriving an optimum low-distortion estimator with models that fit well with speech and noise data signals. This estimator provides minimum levels of speech distortion and residual noise with additional improvements in speech perceptual aspects via four key steps. First, a recent transform based on an orthogonal polynomial is used to transform the observation signal into a transform domain. Second, the noise classification based on feature extraction is adopted to find accurate and mutable models for noise signals. Third, two stages of nonlinear and linear estimators based on the minimum mean square error (MMSE) and new models for speech and noise are derived to estimate a clean speech signal. Finally, the estimated speech signal in the time domain is determined by considering the inverse of the orthogonal transform. The results show that the average classification accuracy of the proposed approach is 99.43%. In addition, the proposed algorithm significantly outperforms existing speech estimators in terms of quality and intelligibility measures

Speech Enhancement Algorithm Based on Super-Gaussian Modeling and Orthogonal Polynomials

Different types of noise from the surrounding always interfere with speech and produce annoying signals for the human auditory system. To exchange speech information in a noisy environment, speech quality and intelligibility must be maintained, which is a challenging task. In most speech enhancement algorithms, the speech signal is characterized by Gaussian or super-Gaussian models, and noise is characterized by a Gaussian prior. However, these assumptions do not always hold in real-life situations, thereby negatively affecting the estimation, and eventually, the performance of the enhancement algorithm. Accordingly, this paper focuses on deriving an optimum low-distortion estimator with models that fit well with speech and noise data signals. This estimator provides minimum levels of speech distortion and residual noise with additional improvements in speech perceptual aspects via four key steps. First, a recent transform based on an orthogonal polynomial is used to transform the observation signal into a transform domain. Second, noise classification based on feature extraction is adopted to find accurate and mutable models for noise signals. Third, two stages of nonlinear and linear estimators based on the minimum mean square error (MMSE) and new models for speech and noise are derived to estimate a clean speech signal. Finally, the estimated speech signal in the time domain is determined by considering the inverse of the orthogonal transform. The results show that the average classification accuracy of the proposed approach is 99.43%. In addition, the proposed algorithm significantly outperforms existing speech estimators in terms of quality and intelligibility measures

Speech enhancement employing Laplacian-Gaussian mixture

73 p.This dissertation reports my work on speech enhancement incorporating statistical modelling of speech signals. In this low complexity speech enhancement algorithm (SEA), a noisy speech signal is first decorrelated and then the clean speech components are estimated from the decorrelated noisy speech samples. The probability density distributions of clean speech and noise signals are assumed to be Laplacian and Gaussian, respectively. The clean speech components are estimated either by Maximum Likelihood (ML) or Minimum-Mean-Square-Error (MMSE) estimators. These estimators require some statistical parameters that can be estimated from the noisy speech. These parameters are adaptively extracted by the ML approach during the active speech or silence intervals, respectively.Master of Science (Signal Processing