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ToolsDasmahapatra, S. and Cox, S. J. (2000) Meta-Models for Confidence Estimation in Speech Recognition. In: IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP '00), 2000-06-05 - 2000-06-09.
Full text not available from this repository. (Request a copy)Abstract
We describe an approach to confidence estimation that attempts to decouple the contributions of the acoustic and language model components to speech recognition output. The output of the acoustic models when decoding phonemes is itself modelled using HMMs to produce a set of models which we term meta-models. When benchmarked against a “standard” method for assigning confidence (the N-best score), the meta-models gave a relative improvement of 6.2%. Furthermore, it appears that the N-best and meta-models techniques are complementary, because they tend to fail on different words
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 26 Aug 2011 13:04 |
| Last Modified: | 22 Apr 2023 04:34 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/22296 |
| DOI: | 10.1109/ICASSP.2000.862107 |
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