Automatic Detection of Well Recognized Words in Automatic Speech Transcriptions
Julie Mauclair, Yannick Estève, Simon Petit-Renaud, Paul 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.- Anthology ID:
- L06-1387
- Volume:
- Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
- Month:
- May
- Year:
- 2006
- Address:
- Genoa, Italy
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2006/pdf/630_pdf.pdf
- DOI:
- Cite (ACL):
- Julie Mauclair, Yannick Estève, Simon Petit-Renaud, and Paul Deléglise. 2006. Automatic Detection of Well Recognized Words in Automatic Speech Transcriptions. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).
- Cite (Informal):
- Automatic Detection of Well Recognized Words in Automatic Speech Transcriptions (Mauclair et al., LREC 2006)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2006/pdf/630_pdf.pdf