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
