A new independent component analysis for speech recognition and separation

Jen-Tzung Chien*, Bo Cheng Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


This paper presents a novel nonparametric likelihood ratio (NLR) objective function for independent component analysis (ICA). This function is derived through the statistical hypothesis test of independence of random observations. A likelihood ratio function is developed to measure the confidence toward independence. We accordingly estimate the demixing matrix by maximizing the likelihood ratio function and apply it to transform data into independent component space. Conventionally, the test of independence was established assuming data distributions being Gaussian, which is improper to realize ICA. To avoid assuming Gaussianity in hypothesis testing, we propose a nonparametric approach where the distributions of random variables are calculated using kernel density functions. A new ICA is then fulfilled through the NLR objective function. Interestingly, we apply the proposed NLR-ICA algorithm for unsupervised learning of unknown pronunciation variations. The clusters of speech hidden Markov models are estimated to characterize multiple pronunciations of subword units for robust speech recognition. Also, the NLR-ICA is applied to separate the linear mixture of speech and audio signals. In the experiments, NLR-ICA achieves better speech recognition performance compared to parametric and nonparametric minimum mutual information ICA.

Original languageEnglish
Pages (from-to)1245-1254
Number of pages10
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number4
StatePublished - 1 Jul 2006


  • Acoustic modeling
  • Blind source separation (BSS)
  • Independent component analysis (ICA)
  • Nonparametric likelihood ratio (NLR)
  • Pronunciation variation
  • Speech recognition
  • Unsupervised learning

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