A reliable data selection for model-based noise suppression using unsupervised joint speaker adaptation and noise model estimation

@article{Masakiyo2012ARD,
  title={A reliable data selection for model-based noise suppression using unsupervised joint speaker adaptation and noise model estimation},
  author={Fujimoto Masakiyo and N. Tomohiro},
  journal={2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2012)},
  year={2012},
  pages={148-153}
}
The performance of model-based noise suppression is significantly affected by variations in speaker characteristics and the modeling accuracy of the noise. As regards this problem, the joint processing of speaker adaptation and accurate noise model estimation are crucial factors for improving model-based noise suppression. However, this joint processing is computationally intractable due to the direct unobservability of clean speech and noise signals in the conventional approach, which… CONTINUE READING
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