Maximum likelihood method for finding an adapted speaker model in eigenvoice space
US6263309A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Apr 30, 1998 |
| Grant date | Jul 17, 2001 |
| Priority date | — |
| Expiry date | Apr 30, 2018 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/07
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A set of speaker dependent models is trained upon a comparatively large number of training speakers, one model per speaker, and model parameters are extracted in a predefined order to construct a set of supervectors, one per speaker. Principle component analysis is then performed on the set of supervectors to generate a set of eigenvectors that define an eigenvoice space. If desired, the number of vectors may be reduced to achieve data compression. Thereafter, a new speaker provides adaptation data from which a supervector is constructed by constraining this supervector to be in the eigenvoice space based on a maximum likelihood estimation. The resulting coefficients in the eigenspace of this new speaker may then be used to construct a new set of model parameters from which an adapted model is constructed for that speaker. Environmental adaptation may be performed by including environmental variations in the training data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.