Unsupervised training of an HMM-based self-organizing unit recognizer with applications to topic classification and keyword discovery

@article{Siu2014UnsupervisedTO,
  title={Unsupervised training of an HMM-based self-organizing unit recognizer with applications to topic classification and keyword discovery},
  author={Man-Hung Siu and Herbert Gish and Arthur Chan and William Belfield and Steve Lowe},
  journal={Computer Speech & Language},
  year={2014},
  volume={28},
  pages={210-223}
}
We present our approach to unsupervised training of speech recognizers. Our approach iteratively adjusts sound units that are ptimized for the acoustic domain of interest. We thus enable the use of speech recognizers for applications in speech domains here transcriptions do not exist. The resulting recognizer is a state-of-the-art recognizer on the optimized units. Specifically we ropose building HMM-based speech recognizers without transcribed data by formulating the HMM training as an… CONTINUE READING
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  • We also report improvements, including the use of context dependent acoustic models and lattice-based features, hat together reduce the topic verification equal error rate from 12% to 7%.

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Segmenting speech using dynamic programming

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Discriminative feature selection using support vector machines

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