Discriminative training for speaker and speech verification
US7454339B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 20, 2005 |
| Grant date | Nov 18, 2008 |
| Priority date | — |
| Expiry date | Jun 25, 2027 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/063
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for discriminatively training acoustic models is provided for automated speaker verification (SV) and speech (or utterance) verification (UV) systems. The method includes: defining a likelihood ratio for a given speech segment, whose speaker identity (for SV system) or linguist identity (for UV system) is known, using a corresponding acoustic model, and an alternative acoustic model which represents all other speakers (in SV) or all other linguist identities (in UV); determining an average likelihood ratio score for the likelihood ratio scores over a set of training utterances (referred to as true data set) whose speaker identities (for SV) or linguist identities (for UV) are the same; determining an average likelihood ratio score for the likelihood ratio scores over a competing set of training utterances which excludes the speech data in the true data set (referred to as competing data set); and optimizing a difference between the average likelihood ratio score over the true data set and the average likelihood ratio score over the competing data set, thereby improving the acoustic model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.