Journal article
Speaker-independent isolated word recognition using multiple hidden Markov models
IEE Proceedings - Vision, Image, and Signal Processing, Vol.141(3), pp.197-202
1994
Abstract
A multi-HMM speaker-independent isolated word recognition system is described. In this system, three vector quantisation methods, the LBG algorithm, the EM algorithm, and a new MGC algorithm, are used for the classification of the speech space. These quantisations of the speech space are then used to produce three HMMs for each word in the vocabulary. In the recognition step, the Viterbi algorithm is used in the three subrecognisers. The log probabilities of the observation sequences matching-the models are multiplied by the weights determined by the recognition accuracies of individual subrecognisers and summed to give the log probability that the utterance is of a particular word in the vocabulary. This multi-HMM system results in a reduction of about 50% in the error rate in comparison with the single model system
Details
- Title
- Speaker-independent isolated word recognition using multiple hidden Markov models
- Authors/Creators
- Y. Zhang (Author/Creator)C.J.S. deSilva (Author/Creator)R. Togneri (Author/Creator)M. Alder (Author/Creator)Y. Attikiouzel (Author/Creator)
- Publication Details
- IEE Proceedings - Vision, Image, and Signal Processing, Vol.141(3), pp.197-202
- Publisher
- Institute of Electrical Engineers
- Identifiers
- 991005540900207891
- Copyright
- © IEE, 1994
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
Metrics
193 File views/ downloads
101 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.174 Digital Signal Processing
- 4.174.152 Speech Recognition
- Web Of Science research areas
- Engineering, Electrical & Electronic
- ESI research areas
- Engineering