Cascaded hidden Markov model for meta-state estimation
US6963835B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 31, 2003 |
| Grant date | Nov 8, 2005 |
| Priority date | — |
| Expiry date | Jul 17, 2023 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L15/142
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and system for training an audio analyzer (114) to identify asynchronous segments of audio types using sample data sets, the sample data sets being representative of audio signals for which segmentation is desired. The system and method then label asynchronous segments of audio samples, collected at the target site, into a plurality of categories by cascading hidden Markov models (HMM). The cascaded HMMs consist of 2 stages, the output of the first stage HMM (208) being transformed and used as observation inputs to the second stage HMM (212). This cascaded HMM approach allows for modeling processes with complex temporal characteristics by using training data. It also contains a flexible framework that allows for segments of varying duration. The system and method are particularly useful in identifying and separating segments of the human voice for voice recognition systems from other audio such as music.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.