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.

Date of Patent:
Aug. 18, 2020

Filed:

Oct. 12, 2018
Applicant:

Promptu Systems Corporation, Menlo Park, CA (US);

Inventors:

Harry Printz, San Francisco, CA (US);

Naren Chittar, Mountain View, CA (US);

Assignee:

Promptu Systems Corporation, Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 15/187 (2013.01); G06F 16/9535 (2019.01); G06Q 30/02 (2012.01); G10L 17/26 (2013.01); G10L 15/18 (2013.01); G10L 15/22 (2006.01); G10L 15/02 (2006.01); G10L 15/14 (2006.01); G06F 16/95 (2019.01);
U.S. Cl.
CPC ...
G10L 15/187 (2013.01); G06F 16/9535 (2019.01); G06Q 30/02 (2013.01); G10L 15/02 (2013.01); G10L 15/142 (2013.01); G10L 15/18 (2013.01); G10L 15/22 (2013.01); G10L 17/26 (2013.01); G06F 16/95 (2019.01); G10L 2015/025 (2013.01);
Abstract

Efficient empirical determination, computation, and use of an acoustic confusability measure comprises: (1) an empirically derived acoustic confusability measure, comprising a means for determining the acoustic confusability between any two textual phrases in a given language, where the measure of acoustic confusability is empirically derived from examples of the application of a specific speech recognition technology, where the procedure does not require access to the internal computational models of the speech recognition technology, and does not depend upon any particular internal structure or modeling technique, and where the procedure is based upon iterative improvement from an initial estimate; (2) techniques for efficient computation of empirically derived acoustic confusability measure, comprising means for efficient application of an acoustic confusability score, allowing practical application to very large-scale problems; and (3) a method for using acoustic confusability measures to make principled choices about which specific phrases to make recognizable by a speech recognition application.


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