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Automatic Accent and Gender Recognition of Regional UK Speakers

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Engineering Applications of Neural Networks (EANN 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1600))

Abstract

With the ubiquity of voice assistants across the UK and the world, speech recognition of the regional accents across the British Isles has proven challenging due to varying pronunciations. This paper proposes an automated recognition of the geographical origin and gender of a voice sample based on the six regional dialects of the United Kingdom. Twenty six features are extracted from 17,877 voice samples and then used to design, implement and evaluate machine learning classifiers based on Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Random Forest (RF) and k-nearest neighbors (k-NN) algorithms. The results suggest that the proposed approach could be applicable for areas such as e-commerce and the service industry, and it provides a contribution to NLP audio research.

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Notes

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Author information

Authors and Affiliations

  1. Teesside University, Middlesbrough, TS1 3BX, UK

    Chrisina Jayne, Jozeene Bailey & Qianwen Ariel Xu

  2. Aston University, Aston St, Birmingham, B4 7ET, UK

    Victor Chang

Authors
  1. Chrisina Jayne
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  2. Victor Chang
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  3. Jozeene Bailey
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  4. Qianwen Ariel Xu
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Corresponding author

Correspondence to Chrisina Jayne .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Teesside University, Middlesbrough, UK

    Chrisina Jayne

  3. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Anastasios Tefas

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Jayne, C., Chang, V., Bailey, J., Xu, Q.A. (2022). Automatic Accent and Gender Recognition of Regional UK Speakers. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_6

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  • DOI: https://doi.org/10.1007/978-3-031-08223-8_6

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