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Speech production knowledge in automatic speech recognition.

Simon King1, Joe Frankel, Karen Livescu

  • 1Centre for Speech Technology Research, University of Edinburgh, 2 Buccleuch Place, Edinburgh EH8 9LW, United Kingdom. Simon.King@ed.ac.uk

The Journal of the Acoustical Society of America
|March 14, 2007
PubMed
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Speech production knowledge, though rich in data and models, is largely overlooked in automatic speech recognition (ASR). Incorporating articulatory representations can significantly enhance ASR system performance and understanding.

Area of Science:

  • Computational Linguistics
  • Speech Science
  • Artificial Intelligence

Background:

  • Extensive research exists on speech production, yielding articulatory data, feature systems, and models.
  • Current mainstream automatic speech recognition (ASR) approaches largely disregard speech production knowledge.

Purpose of the Study:

  • To survey and highlight the growing body of work integrating speech production representations into ASR.
  • To demonstrate how articulatory information can improve ASR performance.

Main Methods:

  • Reviewing studies that utilize speech production models and data within ASR frameworks.
  • Analyzing the impact of articulatory features on speech recognition accuracy.

Main Results:

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  • Speech production representations offer explanations for phenomena not evident from acoustic signals or phonetic transcriptions alone.
  • Integrating articulatory knowledge into ASR leads to improved recognition capabilities.

    • Conclusions:

    • Speech production knowledge is a valuable, yet underutilized, resource for advancing automatic speech recognition.
    • Future ASR development should more effectively incorporate articulatory and production-based insights.