Enhancing phoneme recogniser performance with a simple rule-based language model

Pertti Väyrynen, Johannes Peltola, Tapio Seppänen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

The phoneme classification inaccuracy at the acoustic phonetic level is a major weakness in most speech recognition systems. However, the inaccuracy will violate phonotactic constraints at the acoustic phonetic level. A better performance is expected if a language model is adopted in a recognition system for post-processing phoneme estimates and making corrections with a set of explicit rules of the language used. We developed a simple language model with a set of rules to correct phoneme classification errors made by a Hidden Markov Model based phoneme recognizer. The experimental results indicate that about 20% of
recognition errors can be corrected.
Original languageEnglish
Title of host publicationSTeP2000 Millennium of Artificial Intelligence
Subtitle of host publicationAl of Tommorrow
Pages171-178
Number of pages7
Publication statusPublished - 2000
MoE publication typeA4 Article in a conference publication
Event9th Finnish Artificial Intelligence Conference : Millennium of Artificial Intelligence, 'AI of Today' - Espoo, Finland
Duration: 28 Aug 200030 Aug 2000
Conference number: 9

Conference

Conference9th Finnish Artificial Intelligence Conference
Abbreviated titleSTeP 2000
CountryFinland
CityEspoo
Period28/08/0030/08/00

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Väyrynen, P., Peltola, J., & Seppänen, T. (2000). Enhancing phoneme recogniser performance with a simple rule-based language model. In STeP2000 Millennium of Artificial Intelligence: Al of Tommorrow (pp. 171-178)