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Voice comparison between smokers and non-smokers using HMM speech recognition system

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Abstract

Automatic speech recognition is a technology that allows a computer to transcribe in real time spoken words into readable text. In this work an HMM automatic speech recognition system was created to detect smoker speaker. This research project is carried out using Amazigh language for comparison of the voice of normal persons to smokers one. To achieve this goal, two experiments were performed, the first one to test the performance of the system for non-smokers for different parameters. The second experiment concern smokers speakers. The corpus used in this system is collected from two groups of speaker, non-smokers and smokers native Morocan tarifit speakers aged between 25 and 55 years. Our experimental results show that we can use our system to make diagnostic for smoking people and confirm that a speaker is smoker when the observed recognition rate is below 50%.

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Funding

Funding was provided by Sidi Mohemed ben abdellah University (Grant No. ID0EMQAE137).

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Authors and Affiliations

  1. Laboratory Computer Science, Image processing and Numerical Analysis, Faculty of Sciences Dhar Mahraz, Sidi Mohammed Ben Abbdallah University, Fez, Morocco

    Hassan Satori, Ouissam Zealouk & Khalid Satori

  2. Department of Mathematics and Computer Science Faculty Polydisciplinary of Nador, Mohammed Premier University, 300, Selouane, 62700, Nador, Morocco

    Hassan Satori & Fatima ElHaoussi

Authors
  1. Hassan Satori
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  2. Ouissam Zealouk
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  3. Khalid Satori
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  4. Fatima ElHaoussi
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Correspondence to Hassan Satori.

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Satori, H., Zealouk, O., Satori, K. et al. Voice comparison between smokers and non-smokers using HMM speech recognition system. Int J Speech Technol 20, 771–777 (2017). https://doi.org/10.1007/s10772-017-9442-0

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  • DOI: https://doi.org/10.1007/s10772-017-9442-0

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