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Estimation of ASR Parameterization for Interactive System

Estimation of ASR Parameterization for Interactive System

Mohamed Hamidi, Hassan Satori, Ouissam Zealouk, Naouar Laaidi
Copyright: © 2021 |Volume: 10 |Issue: 1 |Pages: 13
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781799861287|DOI: 10.4018/IJNCR.2021010103
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MLA

Hamidi, Mohamed, et al. "Estimation of ASR Parameterization for Interactive System." IJNCR vol.10, no.1 2021: pp.28-40. http://doi.org/10.4018/IJNCR.2021010103

APA

Hamidi, M., Satori, H., Zealouk, O., & Laaidi, N. (2021). Estimation of ASR Parameterization for Interactive System. International Journal of Natural Computing Research (IJNCR), 10(1), 28-40. http://doi.org/10.4018/IJNCR.2021010103

Chicago

Hamidi, Mohamed, et al. "Estimation of ASR Parameterization for Interactive System," International Journal of Natural Computing Research (IJNCR) 10, no.1: 28-40. http://doi.org/10.4018/IJNCR.2021010103

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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.

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