AN SVM BASED CLASSIFICATION APPROACH TO SPEECH SEPARATION

Speech Enhancement

Presented by: Kun Han, Author(s): Kun Han, DeLiang Wang, The Ohio State University, United States

Monaural speech separation is a very challenging task. CASA-based systems utilize acoustic features to produce a time-frequency (T-F) mask. In this study, we propose a classification approach to monaural separation problem. Our feature set consists of pitch-based features and amplitude modulation spectrum features, which can discriminate both voiced and unvoiced speech from nonspeech interference. We employ support vector machines (SVMs) followed by a re-thresholding method to classify each T-F unit as either target-dominated or interference-dominated. An auditory segmentation stage is then utilized to improve SVM-generated results. Systematic evaluations show that our approach produces high quality binary masks and outperforms a previous system in terms of classification accuracy.

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