Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Continuous speech recognition from a phonetic transcription
HLT '90 Proceedings of the workshop on Speech and Natural Language
Acoustic modeling of subword units for large vocabulary speaker independent speech recognition
HLT '89 Proceedings of the workshop on Speech and Natural Language
Tied mixtures in the Lincoln robust CSR
HLT '89 Proceedings of the workshop on Speech and Natural Language
Automatic speech recognition and speech variability: A review
Speech Communication
A new algorithm for Arabic optical character recognition
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Adaptation of large vocabulary recognition system parameters
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
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In this paper we describe the algorithms used in the BBN BYBLOS Continuous Speech Recognition system. The BYBLOS system uses context-dependent hidden Markov models of phonemes to provide a robust model of phonetic coarticulation. We provide an update of the ongoing research aimed at improving the recognition accuracy. In the first experiment we confirm the large improvement in accuracy that can be derived by using spectral derivative parameters in the recognition. In particular, the word error rate is reduced by a factor of two. Currently the system achieves a word error rate of 2.9% when tested on the speaker-dependent part of the standard 1000-Word DARPA Resource Management Database using the Word-Pair grammar supplied with the database. When no grammar was used, the error rate is 15.3%. Finally, we present a method for smoothing the discrete densities on the states of the HMM, which is intended to alleviate the problem of insufficient training for detailed phonetic models.