Supervised Speech Separation and Processing

In real-world environments, speech often occurs simultaneously with acoustic interference, such as background noise or reverberation. The interference usually leads to adverse effects on speech perception, and results in performance degradation in many speech applications, including automatic speech recognition and speaker identification. Monaural speech separation and processing aim to separate or analyze speech from interference based on only one recording. Although significant progress has been made on this problem, it is a widely regarded challenge. Unlike traditional signal processing, this dissertation addresses the speech separation and processing problems using machine learning techniques. We first propose a classification approach to estimate the ideal binary mask (IBM) which is considered as a main goal of sound separation in computational auditory scene analysis (CASA). We employ support vector machines (SVMs) to classify time-frequency (T-F) units as either target-dominant or interference-dominant. A rethresholding method is incorporated to improve classification results and maximize hit minus false alarm rates.