Meta-Models for Confidence Estimation in Speech Recognition

S. Dasmahapatra, S. J. Cox

Research output: Contribution to conferencePaper

3 Citations (Scopus)

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
Original languageEnglish
Pages1815-1818
Number of pages4
DOIs
Publication statusPublished - Jun 2000
EventIEEE Conference on Acoustics, Speech and Signal Processing (ICASSP '00) - Istanbul, Turkey
Duration: 5 Jun 20009 Jun 2000

Conference

ConferenceIEEE Conference on Acoustics, Speech and Signal Processing (ICASSP '00)
CountryTurkey
CityIstanbul
Period5/06/009/06/00

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