Tandem connectionist feature extraction for conventional HMM systems

@article{Hermansky2000TandemCF,
  title={Tandem connectionist feature extraction for conventional HMM systems},
  author={Hynek Hermansky and Daniel P. W. Ellis and Sangita Sharma},
  journal={2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)},
  year={2000},
  volume={3},
  pages={1635-1638 vol.3}
}
  • H. Hermansky, D. Ellis, Sangita Sharma
  • Published 5 June 2000
  • Computer Science
  • 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)
Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decorrelated acoustic feature vectors that correspond to individual subword units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units given the acoustic observations. In this work we show a large improvement in word recognition performance by combining neural-net discriminative… 
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