Abstract
This paper presents a novel fused hidden Markov model (fused HMM) for integrating tightly coupled time series, such as audio and visual features of speech. In this model, the time series are first modeled by two conventional HMMs separately. The resulting HMMs are then fused together using a probabilistic fusion model, which is optimal according to the maximum entropy principle and a maximum mutual information criterion. Simulations and bimodal speaker verification experiments show that the proposed model can significantly reduce the recognition errors in noiseless or noisy environments.
Original language | English (US) |
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Pages (from-to) | 573-581 |
Number of pages | 9 |
Journal | IEEE Transactions on Signal Processing |
Volume | 52 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2004 |
Keywords
- Bimodal speech processing
- Hidden Markov model
- Information fusion
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing