Meta-Models for Confidence Estimation in Speech Recognition

Dasmahapatra, 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)


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
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 26 Aug 2011 13:04
Last Modified: 02 Oct 2022 23:49
DOI: 10.1109/ICASSP.2000.862107

Actions (login required)

View Item View Item