ISCA Archive Interspeech 2006
ISCA Archive Interspeech 2006

Incremental learning of MAP context-dependent edit operations for spoken phone number recognition in an embedded platform

Hahn Koo, Yan Ming Cheng

Error-corrective post-processing (ECPP) has great potential to reduce speech recognition errors beyond that obtained by speech model improvement. ECPP approaches aim to learn error-corrective rules to directly reduce speech recognition errors. This paper presents our investigation into one such approach, incremental learning of maximum a posteriori (MAP) context-dependent edit operations. Limiting our dataset to spoken telephone number recognition output, we have evaluated this approach in an automotive environment using an embedded speech recognizer in a mobile device. We have found that a reduction of approximately 44¡«49% in speech recognition string errors can be achieved after learning.


doi: 10.21437/Interspeech.2006-94

Cite as: Koo, H., Cheng, Y.M. (2006) Incremental learning of MAP context-dependent edit operations for spoken phone number recognition in an embedded platform. Proc. Interspeech 2006, paper 1032-Thu1CaP.3, doi: 10.21437/Interspeech.2006-94

@inproceedings{koo06_interspeech,
  author={Hahn Koo and Yan Ming Cheng},
  title={{Incremental learning of MAP context-dependent edit operations for spoken phone number recognition in an embedded platform}},
  year=2006,
  booktitle={Proc. Interspeech 2006},
  pages={paper 1032-Thu1CaP.3},
  doi={10.21437/Interspeech.2006-94},
  issn={2958-1796}
}