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Smart Computing Paradigms: New Progresses and Challenges pp 131–138Cite as

Sentence-Based Dialect Identification System Using Extreme Gradient Boosting Algorithm

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 766))

Abstract

In this paper, a dialect identification system (DIS) is proposed by exploring the dialect specific prosodic features and cepstral coefficients from sentence-level utterances. Commonly, people belonging to a specific region follow a unique speaking style among them known as dialects. Sentence speech units are chosen for dialect identification since it is observed that a unique intonation and energy patterns are followed in sentences. Sentences are derived from a standard Intonational Variations in English (IViE) speech dataset. In this paper, pitch and energy contour are used to derive intonation and energy features respectively by using Legendre polynomial fit function along with five statistical features. Further, Mel frequency cepstral coefficients (MFCCs) are added to capture dialect specific spectral information. Extreme Gradient Boosting (XGB) ensemble method is employed for evaluation of the system under individual and combinations of features. Obtained results have indicated the influences of both prosodic and spectral features in recognition of dialects, also combined feature vectors have shown a better DIS performance of about 89.6%.

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Author information

Authors and Affiliations

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

    Nagaratna B. Chittaragi

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

    Nagaratna B. Chittaragi & Shashidhar G. Koolagudi

Authors
  1. Nagaratna B. Chittaragi
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  2. Shashidhar G. Koolagudi
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Corresponding author

Correspondence to Nagaratna B. Chittaragi .

Editor information

Editors and Affiliations

  1. Engineering Faculty, Aksaray University, Sağlık, Aksaray, Turkey

    Atilla Elçi

  2. Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    Pankaj Kumar Sa

  3. National Institute of Technology, Goa, India

    Chirag N. Modi

  4. CICESE, Ensenada, Baja California, Mexico

    Gustavo Olague

  5. Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    Manmath N. Sahoo

  6. Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    Sambit Bakshi

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Chittaragi, N.B., Koolagudi, S.G. (2020). Sentence-Based Dialect Identification System Using Extreme Gradient Boosting Algorithm. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 766. Springer, Singapore. https://doi.org/10.1007/978-981-13-9683-0_14

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