Speech processing using conditional observable maximum likelihood continuity mapping
Abstract
A computer implemented method enables the recognition of speech and speech characteristics. Parameters are initialized of first probability density functions that map between the symbols in the vocabulary of one or more sequences of speech codes that represent speech sounds and a continuity map. Parameters are also initialized of second probability density functions that map between the elements in the vocabulary of one or more desired sequences of speech transcription symbols and the continuity map. The parameters of the probability density functions are then trained to maximize the probabilities of the desired sequences of speech-transcription symbols. A new sequence of speech codes is then input to the continuity map having the trained first and second probability function parameters. A smooth path is identified on the continuity map that has the maximum probability for the new sequence of speech codes. The probability of each speech transcription symbol for each input speech code can then be output.
- Inventors:
- Issue Date:
- Research Org.:
- Univ. of California, Oakland, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1174686
- Patent Number(s):
- 6678658
- Assignee:
- University Of California, The Regents Of
- Patent Classifications (CPCs):
-
G - PHYSICS G10 - MUSICAL INSTRUMENTS G10L - SPEECH ANALYSIS OR SYNTHESIS
- Resource Type:
- Patent
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Hogden, John, and Nix, David. Speech processing using conditional observable maximum likelihood continuity mapping. United States: N. p., 2004.
Web.
Hogden, John, & Nix, David. Speech processing using conditional observable maximum likelihood continuity mapping. United States.
Hogden, John, and Nix, David. Tue .
"Speech processing using conditional observable maximum likelihood continuity mapping". United States. https://www.osti.gov/servlets/purl/1174686.
@article{osti_1174686,
title = {Speech processing using conditional observable maximum likelihood continuity mapping},
author = {Hogden, John and Nix, David},
abstractNote = {A computer implemented method enables the recognition of speech and speech characteristics. Parameters are initialized of first probability density functions that map between the symbols in the vocabulary of one or more sequences of speech codes that represent speech sounds and a continuity map. Parameters are also initialized of second probability density functions that map between the elements in the vocabulary of one or more desired sequences of speech transcription symbols and the continuity map. The parameters of the probability density functions are then trained to maximize the probabilities of the desired sequences of speech-transcription symbols. A new sequence of speech codes is then input to the continuity map having the trained first and second probability function parameters. A smooth path is identified on the continuity map that has the maximum probability for the new sequence of speech codes. The probability of each speech transcription symbol for each input speech code can then be output.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 13 00:00:00 EST 2004},
month = {Tue Jan 13 00:00:00 EST 2004}
}
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