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Image-based features for speech signal classification

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Abstract

Like other applications, under the purview of pattern classification, analyzing speech signals is crucial. People often mix different languages while talking which makes this task complicated. This happens mostly in India, since different languages are used from one state to another. Among many, Southern part of India suffers a lot from this situation, where distinguishing their languages is important. In this paper, we propose image-based features for speech signal classification because it is possible to identify different patterns by visualizing their speech patterns. Modified Mel frequency cepstral coefficient (MFCC) features namely MFCC- Statistics Grade (MFCC-SG) were extracted which were visualized by plotting techniques and thereafter fed to a convolutional neural network. In this study, we used the top 4 languages namely Telugu, Tamil, Malayalam, and Kannada. Experiments were performed on more than 900 hours of data collected from YouTube leading to over 150000 images and the highest accuracy of 94.51% was obtained.

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Authors and Affiliations

  1. Department of Computer Science, West Bengal State University, Kolkata, India

    Himadri Mukherjee, Ankita Dhar & Kaushik Roy

  2. Department of Computer Science and Engineering, Aliah University, Kolkata, India

    Sk Md Obaidullah

  3. Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India

    Santanu Phadikar

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  1. Himadri Mukherjee
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  2. Ankita Dhar
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  3. Sk Md Obaidullah
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  4. Santanu Phadikar
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  5. Kaushik Roy
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Correspondence to Ankita Dhar.

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Mukherjee, H., Dhar, A., Obaidullah, S.M. et al. Image-based features for speech signal classification. Multimed Tools Appl 79, 34913–34929 (2020). https://doi.org/10.1007/s11042-019-08553-6

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  • DOI: https://doi.org/10.1007/s11042-019-08553-6

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