Efficient Empirical Determination, Computation, and Use of Acoustic Confusability Measures

Patent №

US 8,959,019

Granted

2015-02-17

Filed 2007

Owner

PROMPTU SYSTEMS CORPORATION

Lab

AI components

5

ml · nlp · vision · speech · kr

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

11932122

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.

Machine learningNatural languageVisionSpeechKnowledge representationG10L 15/187G06F 16/9535G06Q 30/02G10L 15/02G10L 15/142G10L 15/18G10L 15/22G10L 17/26+2 more

AI classification

Speech1.00
Natural language1.00
Machine learning1.00
Knowledge representation1.00
Vision0.86
AI hardware0.24
Planning0.21
Evolutionary computation0.01

Ownership

PROMPTU SYSTEMS CORPORATION

assignment · 204500576

Assignors

PRINTZ, HARRY, CHITTAR, NARREN

On an employer assignment, the assignors are typically the inventors.

From the same owner

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