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Dialect Identification Using Spectral and Prosodic Features on Single and Ensemble Classifiers

  • Research Article - Special Issue - Computer Engineering and Computer Science
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Abstract

In this paper, investigation of the significance of spectral and prosodic behaviors of speech signal has been carried out for dialect identification. Spectral features such as cepstral coefficients, spectral flux, and entropy are extracted from shorter frames. Prosodic attributes such as pitch, energy, and duration are derived from longer frames. IViE (Intonational Variations in English) speech corpus covering nine dialectal regions of British Isles has been considered, to evaluate the proposed approach. Since corpus is available in both read and semi-spontaneous modes, the influence of spectral and prosodic behavior over these datasets is distinguishably articulated. Further, two distinct classification algorithms, namely support vector machine (SVM) and an ensemble of decision trees along with the SVM are used for identification of nine dialects. Dialect discriminating information captured from both features are used for constructing feature vectors. Experiments have been conducted on individual and combinations of features. A better dialect recognition performance is observed with ensemble methods over a single independent SVM.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, 575025, India

    Nagaratna B. Chittaragi & Shashidhar G. Koolagudi

  2. Department of Information Science and Engineering, Siddaganga Institute of Technology, Tumkur, India

    Nagaratna B. Chittaragi

  3. Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, Karnataka, 575025, India

    Ambareesh Prakash

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  1. Nagaratna B. Chittaragi
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  2. Ambareesh Prakash
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  3. Shashidhar G. Koolagudi
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Correspondence to Nagaratna B. Chittaragi.

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Chittaragi, N.B., Prakash, A. & Koolagudi, S.G. Dialect Identification Using Spectral and Prosodic Features on Single and Ensemble Classifiers. Arab J Sci Eng 43, 4289–4302 (2018). https://doi.org/10.1007/s13369-017-2941-0

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  • DOI: https://doi.org/10.1007/s13369-017-2941-0

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