A robust connected-words recognizer
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
HMM modeling for speaker independent voice dialing in car environment
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Segmental GPD training of HMM based speech recognizer
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
High performance connected digit recognition using codebook exponents
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
A two pass classifier for utterance rejection in keyword spotting
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Minimum error rate training based on N-best string models
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
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It is shown how one can apply the improved acoustic modeling techniques (using a continuous density hidden Markov model framework) developed for large vocabulary speech recognition applications to the problem of connected digit recognition with no changes made to the basic modeling techniques and with no vocabulary specific information used. The improved modeling techniques adopted in this study include an improved feature analysis procedure, which incorporates higher order cepstral and log energy time derivatives, and an improved acoustic resolution procedure, which uses more Gaussian mixture components per state to characterize the acoustic variability in each state of the model. Using these techniques, string accuracies of 98.6% for unknown length strings and 99.2% for known length strings were achieved on the standard Texas Instruments connected digits database.