Cascaded hidden Markov model for meta-state estimation

Details Cascaded hidden Markov model for meta-state estimation Cascaded hidden Markov model for meta-state estimation

2005-11-08

2005-11-08

Abebe, Daniel (Department: 2655)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S246000

Reexamination Certificate

active

06963835

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.

REFERENCES:
patent: 5530950 (1996-06-01), Medan et al.
patent: 5594834 (1997-01-01), Wang
patent: 5812973 (1998-09-01), Wang
PCT International Search Report dated Jan. 14, 2005 of International Application No. PCT/US04/09719 filed Mar. 30, 2004.

Affiliated with

Also associated with

Rating

Cascaded hidden Markov model for meta-state estimation does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Cascaded hidden Markov model for meta-state estimation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Cascaded hidden Markov model for meta-state estimation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3514985