Broad phonetic classification using discriminative Bayesian networks
Speech Communication
A survey of techniques for incremental learning of HMM parameters
Information Sciences: an International Journal
| Hi-index | 754.84 |
Some relations among approaches that have been applied to estimating models for acoustic signals in speech recognition systems are examined. In particular, the modeling approaches based on maximum likelihood (ML), maximum mutual information (MMI), and minimum discrimination information (MDI) are studied. It is shown that all three approaches can be formulated uniformly as MDI modeling approaches for simultaneous estimation of the acoustic models for all words in the vocabulary and that none of the approaches requires any model correctness assumption. The three approaches differ in the effective source being modeled and in the probability distribution attributed to this source