Robust HMM phoneme modeling for different speaking styles
- 1 January 1991
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15206149,p. 265-268 vol. 1
- https://doi.org/10.1109/icassp.1991.150328
Abstract
The authors describe the robustness of six types of phoneme-based HMMs (hidden Markov models) against speaking-style variations. The six types of models are VQ (vector quantization)-based and fuzzy VQ-based discrete HMMs, and single-Gaussian and mixture-Gaussian HMMs with either diagonal or full covariance matrices. The mixture-Gaussian HMM with diagonal covariance matrices, the fuzzy VQ-based discrete HMM, and the single-Gaussian HMM with full covariance matrices show better results than the other three in 18-Japanese-consonant recognition experiments. The authors also propose a model-adaptation technique that combines multiple models using the deleted interpolation. This technique makes models easy to apply to different-speaking-style speech.Keywords
This publication has 2 references indexed in Scilit:
- Multi-style training for robust isolated-word speech recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Shift-invariant, multi-category phoneme recognition using Kohonen's LVQ2Published by Institute of Electrical and Electronics Engineers (IEEE) ,2003