Using KL-based Acoustic Models in a Large Vocabulary Recognition Task
| Type of publication: | Idiap-RR |
| Citation: | aradilla:rr08-14 |
| Number: | Idiap-RR-14-2008 |
| Year: | 2008 |
| Institution: | IDIAP |
| Abstract: | Posterior probabilities of sub-word units have been shown to be an effective front-end for ASR. However, attempts to model this type of features either do not benefit from modeling context-dependent phonemes, or use an inefficient distribution to estimate the state likelihood. This paper presents a novel acoustic model for posterior features that overcomes these limitations. The proposed model can be seen as a HMM where the score associated with each state is the KL divergence between a distribution characterizing the state and the posterior features from the test utterance. This KL-based acoustic model establishes a framework where other models for posterior features such as hybrid HMM/MLP and discrete HMM can be seen as particular cases. Experiments on the WSJ database show that the KL-based acoustic model can significantly outperform these latter approaches. Moreover, the proposed model can obtain comparable results to complex systems, such as HMM/GMM, using significantly fewer parameters. |
| Userfields: | ipdmembership={speech}, |
| Keywords: | |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
