Phoneme aware speech recognition through evolutionary optimisation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Phoneme awareness provides the path to high resolution speech recognition to overcome the difficulties of classical word recognition. Here we present the results of a preliminary study on Artificial Neural Network (ANN) and Hidden Markov Model (HMM) methods of classification for Human Speech Recognition through Diphthong Vowel sounds in the English Phonetic Alphabet, with a specific focus on evolutionary optimisation of bio-inspired classification methods. A set of audio clips are recorded by subjects from the United Kingdom and Mexico. For each recording, the data were pre-processed, using Mel-Frequency Cepstral Coefficients (MFCC) at a sliding window of 200ms per data object, as well as a further MFCC timeseries format for forecast-based models, to produce the dataset. We found that an evolutionary optimised deep neural network achieves 90.77% phoneme classification accuracy as opposed to the best HMM of 150 hidden units achieving 86.23% accuracy. Many of the evolutionary solutions take substantially longer to train than the HMM, however one solution scoring 87.5% (+1.27%) requires fewer resources than the HMM.

Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PublisherACM
Pages362-363
Number of pages2
ISBN (Electronic)9781450367486
DOIs
Publication statusPublished - 13 Jul 2019
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Publication series

NameGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
CountryCzech Republic
CityPrague
Period13/07/1917/07/19

Keywords

  • Artificial Neural Networks
  • Computational Linguistics
  • Evolutionary Optimisation
  • Phoneme Awareness
  • Speech Recognition

Fingerprint Dive into the research topics of 'Phoneme aware speech recognition through evolutionary optimisation'. Together they form a unique fingerprint.

  • Cite this

    Bird, J. J., Wanner, E., Ekárt, A., & Faria, D. R. (2019). Phoneme aware speech recognition through evolutionary optimisation. In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 362-363). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). ACM. https://doi.org/10.1145/3319619.3321951