UNSUPERVISED AND ACTIVE LEARNING IN AUTOMATIC SPEECH RECOGNITION FOR CALL CLASSIFICATION

Patent №

US 9,666,182

Granted

2017-05-30

Filed 2015

Owner

AT&T CORP.

+2 more

Lab

AI components

4

ml · nlp · speech · hardware

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

14874843

Utterance data that includes at least a small amount of manually transcribed data is provided. Automatic speech recognition is performed on ones of the utterance data not having a corresponding manual transcription to produce automatically transcribed utterances. A model is trained using all of the manually transcribed data and the automatically transcribed utterances. A predetermined number of utterances not having a corresponding manual transcription are intelligently selected and manually transcribed. Ones of the automatically transcribed data as well as ones having a corresponding manual transcription are labeled. In another aspect of the invention, audio data is mined from at least one source, and a language model is trained for call classification from the mined audio data to produce a language model.

Machine learningNatural languageSpeechAI hardwareG10L 15/063G10L 15/07G10L 15/18G10L 15/26G10L 2015/0638

AI classification

Speech1.00
Natural language1.00
Machine learning1.00
AI hardware0.72
Vision0.23
Knowledge representation0.03
Planning0.01
Evolutionary computation0.00

Ownership

AT&T CORP.

assignment · 369380113

AT&T PROPERTIES, LLC

assignment · 370310152

AT&T INTELLECTUAL PROPERTY II, L.P.

assignment · 370320001

Assignors

HAKKANI-TUR, DILEK Z., RAHIM, MAZIN G., RICCARDI, GIUSEPPE, TUR, GOKHAN

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

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