Detecting mis-recognitions in ASR output
Detecting incorrect words in automatic transcriptions can be useful for many applications: to mark or discard low-confidence words in automatic news subtitles or transcriptions, to select unsupervised material to train acoustic models, etc. In this talk, I will report experiments where various statistical classifiers were compared: a baseline Maximum
Entropy approach, Conditional Random Fields, and a Markov Chain approach. New features gathered from other knowledge sources than the decoder itself were explored: a binary feature that compares outputs from two different ASR systems (word by word), a feature based on the number of hits of the hypothesized bigrams, obtained by queries entered into a very popular Web search engine, and finally a feature related to automatically infered topics at sentence and word levels. A classification error rate improvement from 13.9% to 12.1% was achieved. Experiments were conducted on a European Portuguese and an American English broadcast news corpus.
Date: 2010-Oct-08 Time: 15:00:00 Room: 336
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INESC-ID ESR Talks – February 2023
If you are a masters/PhD student or a postdoctoral fellow, come and present your work in an informal and friendly environment – and savour some tasty snacks!
Individual talks will be 10-15 minutes plus time for feedback. Enroll on your selected date by emailing pedro.ferreira[at]inesc-id.pt.
Happening on the second Wednesday of every month (4pm-5pm):
- 15 February (Alves Redol, Room 9)
- 15 March (Alves Redol, Room 9)
- 12 April (Alves Redol, Room 9)
- 10 May (Alves Redol, Room 9)
- 14 June (Alves Redol, Room 9)
- 12 July (Alves Redol, Room 9)
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