Unsupervised and active learning in automatic speech recognition for call classification
View Patent ↗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.
1. A method comprising:
performing, via a processor, 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, via the processor, a predetermined number of utterances not having a corresponding manual transcription based on a geometrically computed n-tuple confidence score; and
generating a language model based on the automatically transcribed utterances, the predetermined number of utterances, and transcriptions of the predetermined number of utterances.
2. The method of claim 1 , wherein the transcriptions of the predetermined number of utterances are made by a human being.
3. The method of claim 1 , further comprising:
performing additional automatic speech recognition using the language model.
4. The method of claim 2 , further comprising:
iteratively repeating the performing of the automatic speech recognition using the bootstrap model, the selecting, and the performing of additional speech recognition using the language model until a word accuracy converges.
5. The method of claim 1 , wherein the predetermined number of utterances correspond to a specific number of utterances having lowest confidence scores.
6. The method of claim 1 , wherein the predetermined number of utterances used in generating the language model are equal in number to the automatically transcribed utterances.
7. The method of claim 1 , wherein the predetermined number of utterances are randomly selected.
8. The method of claim 1 , wherein the language model is further based on the bootstrap model.
9. A system comprising:
a processor; and
a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations 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 web site 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; and
generating a language model based on the automatically transcribed utterances, the predetermined number of utterances, and transcriptions of the predetermined number of utterances.
10. The system of claim 9 , wherein the transcriptions of the predetermined number of utterances are made by a human being.
11. The system of claim 9 , the computer-readable storage medium having additional instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
performing additional automatic speech recognition using the language model.
12. The system of claim 11 , the computer-readable storage medium having additional instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
iteratively repeating the performing of the automatic speech recognition using the bootstrap model, the selecting, and the performing of additional speech recognition using the language model until a word accuracy converges.
13. The system of claim 9 , wherein the predetermined number of utterances correspond to a specific number of utterances having lowest confidence scores.
14. The system of claim 9 , wherein the predetermined number of utterances used in generating the language model are equal in number to the automatically transcribed utterances.
15. The system of claim 9 , wherein the predetermined number of utterances are randomly selected.
16. The system of claim 9 , wherein the language model is further based on the bootstrap model.
17. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations 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; and
generating a language model based on the automatically transcribed utterances, the predetermined number of utterances, and transcriptions of the predetermined number of utterances.
18. The computer-readable storage device of claim 17 , wherein the transcriptions of the predetermined number of utterances are made by a human being.
19. The computer-readable storage device of claim 17 , having additional instructions stored which, when executed by the computing device, cause the computing device to perform operations comprising:
performing additional automatic speech recognition using the language model.
20. The computer-readable storage device of claim 17 , having additional instructions stored which, when executed by the computing device, cause the computing device to perform operations comprising:
iteratively repeating the performing of the automatic speech recognition using the bootstrap model, the selecting, and the performing of additional speech recognition using the language model until a word accuracy converges.