Classifying the speech response of the brain using Gaussian hidden markov model (HMM) with independent component analysis (ICA)

Annu Int Conf IEEE Eng Med Biol Soc. 2013:2013:4291-4. doi: 10.1109/EMBC.2013.6610494.

Abstract

The purpose of this paper is to determine whether electroencephalograpy (EEG) can be used as a tool for hearing impairment tests such as hearing screening. For this study, we recorded EEG responses to two syllables, /a/ and /u/, in Korean from three subjects at Gwangju Institute of Science and Technology. The ultimate goal of this study is to classify speech sound data regardless of their size using EEG; however, as an initial stage of the study, we classified only two different speech syllables using Gaussian hidden markov model. The result of this study shows a possibility that EEG could be used for hearing screening and other diagnostic tools related to speech perception.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acoustic Stimulation
  • Algorithms*
  • Brain / physiology*
  • Humans
  • Male
  • Markov Chains*
  • Normal Distribution
  • Phonetics
  • Speech / physiology*
  • Speech Perception
  • Time Factors