LREC 2006 - Proceedings sorted by papers

Title	Automatic Detection of Well Recognized Words in Automatic Speech Transcriptions
Authors	J. Mauclair, Y. Estève, S. Petit-renaud, P. Deléglise
Abstract	This work adresses the use of confidence measures for extracting well recognized words with very low error rate from automatically transcribed segments in a unsupervised way. We present and compare several confidence measures and propose a method to merge them into a new one. We study its capabilities on extracting correct recognized word-segments compared to the amount of rejected words. We apply this fusion measure to select audio segments composed of words with a high confidence score. These segments come from an automatic transcription of french broadcast news given by our speech recognition system based on the CMU Sphinx3.3 decoder. Injecting new data resulting from unsupervised treatments of raw audio recordings in the training corpus of acoustic models gives statistically significant improvement (95% confident interval) in terms of word error rate. Experiments have been carried out on the corpus used during ESTER, the french evaluation campaign.
Keywords	Confidence measure, Merging, Filtering data, Training in an unsupervised way
Full paper	Automatic Detection of Well Recognized Words in Automatic Speech Transcriptions

Title

Automatic Detection of Well Recognized Words in Automatic Speech Transcriptions

Authors

J. Mauclair, Y. Estève, S. Petit-renaud, P. Deléglise

Abstract

This work adresses the use of confidence measures for extracting well recognized words with very low error rate from automatically transcribed segments in a unsupervised way. We present and compare several confidence measures and propose a method to merge them into a new one. We study its capabilities on extracting correct recognized word-segments compared to the amount of rejected words. We apply this fusion measure to select audio segments composed of words with a high confidence score. These segments come from an automatic transcription of french broadcast news given by our speech recognition system based on the CMU Sphinx3.3 decoder. Injecting new data resulting from unsupervised treatments of raw audio recordings in the training corpus of acoustic models gives statistically significant improvement (95% confident interval) in terms of word error rate. Experiments have been carried out on the corpus used during ESTER, the french evaluation campaign.

Keywords

Confidence measure, Merging, Filtering data, Training in an unsupervised way

Full paper

Automatic Detection of Well Recognized Words in Automatic Speech Transcriptions