Analysis of speaker variability.

@inproceedings{Huang2001AnalysisOS,
  title={Analysis of speaker variability.},
  author={Chao Huang and Tao Chen and Stan Z. Li and Eric Chang and Jian-Lai Zhou},
  booktitle={INTERSPEECH},
  year={2001}
}
Analysis and modeling of speaker variability, such as gender, accent, age, speech rate, and phones realizations, are important issues in speech recognition. It is known that existing feature representations describing speaker variations can be of very high dimension. In this paper, we introduce two powerful multivariate statistical analysis methods, namely, principal component analysis (PCA) and independent component analysis (ICA), as tools for analysis of such variability and extraction of… CONTINUE READING
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Key Quantitative Results

  • Using 2dimensional ICA representation, we achieved about 6.1% and 13.3% error rate in gender and accent classification, respectively, for 980 speakers.

Citations

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References

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Rapid speaker adaptation in eigenvoice space

IEEE Trans. Speech and Audio Processing • 2000
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Understanding and adapting to speaker variability using correlation-based principal component analysis

Z. H. Hu
Dissertation of OGI. Oct • 1999
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Analysis of a complex of statistical variables into principle components

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