Accent classification in human speech biometrics for native and non-native english speakers

Jordan J. Bird, Elizabeth Wanner, Anikó Ekárt, Diego R. Faria

Research output: Chapter in Book/Published conference outputConference publication


Accent classification provides a biometric path to high resolution speech recognition. This preliminary study explores various methods of human accent recognition through classification of locale. Classical, ensemble, timeseries and deep learning techniques are all explored and compared. A set of diphthong vowel sounds are recorded from participants from the United Kingdom and Mexico, and then formed into a large static dataset of statistical descriptions by way of their Mel-frequency Cepstral Coefficients (MFCC) at a sample window length of 0.02 seconds. Using both flat and timeseries data, various machine learning models are trained and compared to the scientific standard Hidden Markov Model (HMM). Results through 10 fold cross validation show that a vote of average probabilities between a Random Forest and Long Short-term Memory Neural Network result in a classification accuracy of 94.74%, outperforming the speech classification standard Hidden Markov Model by a 5% increase in accuracy.
Original languageEnglish
Title of host publicationProceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2019
Number of pages7
ISBN (Electronic)9781450362320
ISBN (Print)978-1-4503-6232-0
Publication statusPublished - 5 Jun 2019
Eventthe 12th ACM International Conference - Rhodes, Greece
Duration: 5 Jun 20197 Jun 2019


Conferencethe 12th ACM International Conference


  • Accent Recognition
  • Biometrics
  • Computational Linguistics
  • Machine Learning
  • Speech Recognition
  • Voice Assistants


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