Small-word pronunciation modeling for Arabic speech recognition: A data-driven approach

Dia Abuzeina*, Wasfi Al-Khatib, Moustafa Elshafei

*Corresponding author for this work

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

1 Scopus citations

Abstract

Incorrect recognition of adjacent small words is considered one of the obstacles in improving the performance of automatic continuous speech recognition systems. The pronunciation variation in the phonemes of adjacent words introduces ambiguity to the triphone of the acoustic model and adds more confusion to the speech recognition decoder. However, small words are more likely to be affected by this ambiguity than longer words. In this paper, we present a data-driven approach to model the small words problem. The proposed method identifies the adjacent small words in the corpus transcription to generate the compound words. The unique compound words are then added to the expanded pronunciation dictionary, as well as to the language model as a new sentence. Results show a significant improvement of 2.16% in the word error rate compared to that of the Baseline speech corpus of Modern Standard Arabic broadcast news.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 7th Asia Information Retrieval Societies Conference, AIRS 2011, Proceedings
Pages529-537
Number of pages9
DOIs
StatePublished - 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7097 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Modern Standard Arabic
  • Speech recognition
  • language model
  • phonetic dictionary
  • pronunciation variation
  • small-word

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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