Estimation of hidden Markov model parameters by minimizing empirical error rate
- 4 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 709-712 vol.2
- https://doi.org/10.1109/icassp.1990.115867
Abstract
An approach for designing a set of acoustic models for speech recognition applications which results in a minimal empirical error rate for a given decoder and training data is studied. In an evaluation of the system for an isolated word recognition task, hidden Markov models (HMMs) are used to characterize the probability density functions of the acoustic signals from the different words in the vocabulary. Decoding is performed by applying the maximum aposteriori decision rule to the acoustic models. The HMMs are estimated by minimizing a differentiable cost function, which approximates the empirical error rate function, using the steepest descent method. The HMMs designed by the minimum empirical error rate approach were used in multispeaker recognition of the English E-set words and compared to models designed by the standard maximum-likelihood estimation approach. The approach increased recognition accuracy from 68.2% to 76.2% on the training set and from 53.4% to 56.4% on an independent set of test data.Keywords
This publication has 12 references indexed in Scilit:
- Maximum mutual information estimation of hidden Markov model parameters for speech recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Hidden Markov models: a guided tourPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- On the relations between modeling approaches for speech recognitionIEEE Transactions on Information Theory, 1990
- On the use of bandpass liftering in speech recognitionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1987
- On the role of spectral transition for speech perceptionThe Journal of the Acoustical Society of America, 1986
- A Maximum Likelihood Approach to Continuous Speech RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1983
- Maximum likelihood estimation for multivariate observations of Markov sourcesIEEE Transactions on Information Theory, 1982
- Large-scale linearly constrained optimizationMathematical Programming, 1978
- A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov ChainsThe Annals of Mathematical Statistics, 1970
- Statistical Inference for Probabilistic Functions of Finite State Markov ChainsThe Annals of Mathematical Statistics, 1966