Speech Enhancement and Representation Employing the Independent Component Analysis

This chapter presents our recent research on the employment of the Independent Component Analysis (ICA) for speech enhancement and speech representation. In speech enhancement part, we consider a single-channel speech signal corrupted by an additive noise. We investigate novel algorithms for improving the conventional ICA-based speech enhancement, referred to as Sparse Code Shrinkage (SCS). The proposed SCS-based algorithms incorporate multiple ICA transformations and distribution models of speech signal. The speech enhancement algorithms are evaluated in terms of segmental SNR and spectral distortion on speech from the TIMIT database corrupted by Gaussian and real-world Subway noise. The proposed algorithms show significant improvements over the conventional SCS and Wiener filtering. In speech representation part, we present an employment of the ICA for speaker recognition in noisy environments. Finally, we show on a noisy speaker recognition task that the combination of the proposed ICA-based speech enhancement and ICA-based speech representation leads to recognition accuracy improvements compared to the conventional enhancement and representation algorithms.

Keywords: Independent Component Analysis (ICA), speech enhancement, Sparse Code Shrinkage, multiple PDF models, multiple transformations, speech representation, speaker recognition, noise