UNSUPERVISED AND ACTIVE LEARNING IN AUTOMATIC SPEECH RECOGNITION FOR CALL CLASSIFICATION

摘要

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.

主权项

1. A method comprising:
performing automatic speech recognition using a bootstrap model on utterance data not having a corresponding manual transcription, to produce automatically transcribed utterances, wherein the bootstrap model is based on text data mined from a website relevant to a specific domain; selecting a predetermined number of utterances not having a corresponding manual transcription based on a geometrically computed n-tuple confidence score; receiving transcriptions of the predetermined number of utterances, wherein the transcriptions are made by a human being; and generating a language model based on the automatically transcribed utterances, the predetermined number of utterances, and the transcriptions.