Subjective comparison and evaluation of speech enhancement algorithms
Speech Communication
Information Retrieval for Music and Motion
Information Retrieval for Music and Motion
Single-Ended Speech Quality Measurement Using Machine Learning Methods
IEEE Transactions on Audio, Speech, and Language Processing
Low-Complexity, Nonintrusive Speech Quality Assessment
IEEE Transactions on Audio, Speech, and Language Processing
Evaluation of Objective Quality Measures for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
Non-intrusive speech quality assessment using several combinations of auditory features
International Journal of Speech Technology
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We propose a new non-intrusive speech quality assessment algorithm based on Support Vector Regression (SVR) and Mel Frequency Cepstral Coefficients (MFCCs). The basic idea is to map the MFCCs into the desired quality score using SVR. The sensitivity of the MFCCs to external noise is exploited to gauge the changes in the speech signal to evaluate its perceptual quality. The use of SVR exploits the advantages of machine learning with the ability to learn complex data patterns for an effective and generalized mapping of features into a perceptual score, in contrast with the oft-utilized feature pooling process in the existing speech quality estimators. Experimental results indicate that the proposed approach outperforms the standard P.563 algorithm for non-intrusive assessment of speech quality with a total of 1792 speech files and the associated subjective scores.