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An experiment of Moroccan dialect speech recognition in noisy environments using PocketSphinx

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Abstract

In this study, we introduce an experimental framework for Moroccan dialect speech recognition under various additive noise conditions using the open-source tool PocketSphinx. We curated a corpus comprising the ten most commonly used greetings in the Moroccan dialect, extracted from telephone conversations. This corpus was recorded with 60 speakers (30 males and 30 females). Each speaker articulated each expression three times in natural and noisy conditions. Feature extraction utilized Mel Scale Cepstral Coefficients (MFCC), and acoustic modeling, based on monophony, was implemented using Hidden Markov Models (HMM). While automatic speech recognition systems demonstrate commendable performance in noise-free conditions, their efficacy noticeably diminishes in the presence of noise.

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Data availability

The data supporting the findings of this study are available upon reasonable request. Researchers interested in accessing the data can contact corresponding author’s Abdelkbir ouisaadane for further information.

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Funding

Funding for this research is not applicable. This study was conducted without any external financial support, and the authors did not receive any funding from external agencies, organizations, or sponsors.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science Polydisciplinary Faculty, Sultan Moulay Slimane University, Benimellal, Morocco

    Abdelkbir Ouisaadane & Said Safi

  2. GREYC Laboratory ENSICAEN School, LAC Laboratory Caen-Normandie University, Caen, France

    Miloud Frikel

Authors
  1. Abdelkbir Ouisaadane
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  2. Said Safi
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  3. Miloud Frikel
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Correspondence to Abdelkbir Ouisaadane.

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Ouisaadane, A., Safi, S. & Frikel, M. An experiment of Moroccan dialect speech recognition in noisy environments using PocketSphinx. Int J Speech Technol 27, 329–339 (2024). https://doi.org/10.1007/s10772-024-10103-x

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