The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Dec. 09, 2008
Filed:
Nov. 28, 2003
Scott E. Axelrod, Mount Kisco, NY (US);
Sreeram Viswanath Balakrishnan, Los Altos, CA (US);
Stanley F. Chen, Yorktown Heights, NY (US);
Yuging Gao, Mount Kisco, NY (US);
Ramesh A. Gopinath, Millwood, NY (US);
Hong-kwang Kuo, Pleasantville, NY (US);
Benoit Maison, White Plains, NY (US);
David Nahamoo, White Plains, NY (US);
Michael Alan Picheny, White Plains, NY (US);
George A. Saon, Old Greenwich, CT (US);
Geoffrey G. Zweig, Ridgefield, CT (US);
Scott E. Axelrod, Mount Kisco, NY (US);
Sreeram Viswanath Balakrishnan, Los Altos, CA (US);
Stanley F. Chen, Yorktown Heights, NY (US);
Yuging Gao, Mount Kisco, NY (US);
Ramesh A. Gopinath, Millwood, NY (US);
Hong-Kwang Kuo, Pleasantville, NY (US);
Benoit Maison, White Plains, NY (US);
David Nahamoo, White Plains, NY (US);
Michael Alan Picheny, White Plains, NY (US);
George A. Saon, Old Greenwich, CT (US);
Geoffrey G. Zweig, Ridgefield, CT (US);
International Business Machines Corporation, Armonk, NY (US);
Abstract
In a speech recognition system, the combination of a log-linear model with a multitude of speech features is provided to recognize unknown speech utterances. The speech recognition system models the posterior probability of linguistic units relevant to speech recognition using a log-linear model. The posterior model captures the probability of the linguistic unit given the observed speech features and the parameters of the posterior model. The posterior model may be determined using the probability of the word sequence hypotheses given a multitude of speech features. Log-linear models are used with features derived from sparse or incomplete data. The speech features that are utilized may include asynchronous, overlapping, and statistically non-independent speech features. Not all features used in training need to appear in testing/recognition.