Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Hidden Markov Model Inversion for Audio-to-Visual Conversion in an MPEG-4 Facial Animation System
Journal of VLSI Signal Processing Systems
Inversion of hidden markov models and application to robust speech recognition
Inversion of hidden markov models and application to robust speech recognition
Multi-stream articulator model with adaptive reliability measure for audio visual speech recognition
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
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This paper presents an inversion algorithm for dynamic Bayesian networks towards robust speech recognition, namely DBNI, which is a generalization of hidden Markov model inversion (HMMI). As a dual procedure of expectation maximization (EM)-based model reestimation, DBNI finds the 'uncontaminated' speech by moving the input noisy speech to the Gaussian means under the maximum likelihood (ML) sense given the DBN models trained on clean speech. This algorithm can provide both the expressive advantage from DBN and the noise-removal feature from model inversion. Experiments on the Aurora 2.0 database show that the hidden feature model (a typical DBN for speech recognition) with the DBNI algorithm achieves superior performance in terms of word error rate reduction.