Linear discriminant analysis for improved large vocabulary continuous speech recognition
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Discriminative analysis for feature reduction in automatic speech recognition
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Phoneme HMMs constrained by frame correlations
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Allophone modeling for vocabulary-independent HMM recognition
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
The Lincoln large-vocabulary stack-decoder HMM CSR
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Vector quantization for the efficient computation of continuous density likelihoods
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Handling signal variability with contextual markovian models
Pattern Recognition Letters
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The Lincoln robust HMM (hidden Markov model) recognizer has been converted from a single Gaussian or Gaussian mixture PDF per state to tied mixtures in which a single set of Gaussians is shared between all states. There were initial difficulties caused by the use of mixture pruning but these were cured by using observation pruning. Fixed weight smoothing of the mixture weights allowed the use of word-boundary-context-dependent triphone models for both speaker-dependent (SD) and speaker-independent (SI) recognition. A second-differential observation stream further improved SI performance but not SD performance. A novel form of phonetic context model, the semiphone, is also introduced. This model significantly reduces the number of states required to model a vocabulary and unifies triphone and diphone modeling.