INDEPENDENT COMPONENT ANALYSIS FOR NOISY SPEECH RECOGNITION

Independent component analysis (ICA) is not only popular for blind source separation but also for unsupervised learning when the observations can be decomposed into some independent components. These components represent the specific speaker, gender, accent, noise or environment, and act as the basis functions to span the vector space of the human voices in different conditions. Different from eigenvoices built by principal component analysis, the proposed independent voices are estimated by ICA algorithm, and are applied for efficient coding of an adapted acoustic model. Since the information redundancy is significantly reduced in independent voices, we effectively calculate a coordinate vector in independent voice space, and estimate the hidden Markov models (HMMs) for speech recognition. In the experiments, we build independent voices from HMMs under different noise conditions, and find that these voices attain larger redundancy reduction than eigenvoices. The noise adaptive HMMs generated by independent voices achieve better recognition performance than those by eigenvoices.