Minimum phoneme error based heteroscedastic linear discriminant analysis for speech recognition

@article{Zhang2005MinimumPE,
  title={Minimum phoneme error based heteroscedastic linear discriminant analysis for speech recognition},
  author={Bing Zhang and Spyridon Matsoukas},
  journal={Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.},
  year={2005},
  volume={1},
  pages={I/925-I/928 Vol. 1}
}
We introduce a discriminative feature analysis method that seeks to minimize phoneme errors in lattice-based training frameworks. This technique, referred to as minimum phoneme error heteroscedastic linear discriminant analysis (MPE-HLDA), is shown to be more robust than traditional LDA methods in high dimensional spaces, and easy to incorporate with existing training procedures, such as HLDA-SAT and discriminative training of hidden Markov models (HMMs). Results on conversational telephone… CONTINUE READING
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