Automatic segmentation of speech recorded in unknown noisy channel characteristics

Abstract

This paper investigates the problem of automatic segmentation of speech recorded in noisy channel corrupted environments. Using an HMM-based speech segmentation algorithm, speech enhancement and parameter compensation techniques previously proposed for robust speech recognition are evaluated and compared for improved segmentation in colored noise. Speech enhancement algorithms considered include: Generalized Spectral Subtraction, Nonlinear Spectral Subtraction, Ephraim–Malah MMSE enhancement, and Auto-LSP Constrained Iterative Wiener filtering. In addition, the Parallel Model Combination (PMC) technique is also compared for additive noise compensation. In telephone environments, we compare channel normalization techniques including Cepstral Mean Normalization (CMN) and Signal Bias Removal (SBR) and consider the coupling of channel compensation with front-end speech enhancement for improved automatic segmentation. Compensation performance is assessed for each method by automatically segmenting TIMIT degraded by additive colored noise (i.e., aircraft cockpit, automobile highway, etc.), telephone transmitted NTIMIT, and cellular telephone transmitted CTIMIT databases.