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Estimation of ASR Parameterization for Interactive System

Author

Listed:
  • Mohamed Hamidi

    (LISAC, Department of Mathematics and Computer Science FSDM, Sidi Mohamed Ben Abdellah University, Morocco)

  • Hassan Satori

    (LISAC, Department of Mathematics and Computer Science FSDM, Sidi Mohamed Ben Abdellah University, Morocco)

  • Ouissam Zealouk

    (LISAC, Department of Mathematics and Computer Science FSDM, Sidi Mohamed Ben Abdellah University, Morocco)

  • Naouar Laaidi

    (LISAC, Department of Mathematics and Computer Science FSDM, Sidi Mohamed Ben Abdellah University, Morocco)

Abstract

In this study, the authors explore the integration of speaker-independent automatic Amazigh speech recognition technology into interactive applications to extract data remotely from a distance database. Based on the combined interactive voice response (IVR) and automatic speech recognition (ASR) technologies, the authors built an interactive speech system to allow users to interact with the interactive system through voice commands. The hidden Markov models (HMMs), Gaussian mixture models (GMMs), and Mel frequency spectral coefficients (MFCCs) are used to develop a speech system based on the ten first Amazigh digits and six Amazigh words. The best-obtained performance is 89.64% by using 3 HMMs and 16 GMMs.

Suggested Citation

  • Mohamed Hamidi & Hassan Satori & Ouissam Zealouk & Naouar Laaidi, 2021. "Estimation of ASR Parameterization for Interactive System," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 10(1), pages 28-40, January.
    • Handle: RePEc:igg:jncr00:v:10:y:2021:i:1:p:28-40
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