A Combination of Maximum Likelihood Bayesian Framework and Discriminative Linear Transforms for Speaker Adaptation
Keywords:
Discriminative linear transforms, maximum-a-posterior adaptation, maximum likelihood linear regression adaptation, speech recognition, speaker adaptationAbstract
Linear transforms are one of the most commonly
used methods to speaker adaptation. In this paper, we present a combinational method of Bayesian framework and maximum likelihood linear regression as well as discriminative method for speaker adaptation. Furthermore significant gains can be obtained using discriminative training for acoustic models. Experiments on supervised adaptation on Persian data show that the combinational method outperforms both Maximum
likelihood linear regression and Bayesian framework. Also the proposed method with discriminative adaptation outperforms previously proposed methods for transform estimation and discriminative training outperforms ML training.
Downloads
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
