On-Talk and Off-Talk Detection: A Discrete Wavelet Transform Analysis of Electroencephalogram

Fasih Haider, Hayakawa Akira, Saturnino Luz, Carl Vogel, Nick Campbell

Research output: Contribution to conferencePaperpeer-review

Abstract

Spoken interaction with a machine results in a behaviour that is not very common in face-to-face human communication: Off-Talk , which is defined as speech utterances that are not directed to an immediate interlocutor, the machine, but to another person or even oneself. It is our contention that a system which is able to detect the Off-Talk utterances can interact with a human in a more efficient manner by acknowledging that the utterances are not directed to the system and hence, not replying to Off-Talk utterances. In this paper, we demonstrate the discrimination power of a wide range of Electroencephalogram (EEG) frequency bands using wavelet transform analysis and propose models for On-Talk and Off-Talk detection using audio and EEG signals, and their fusion. Our study shows that the EEG signal can identify the occurrence of Off-Talk utterances with promising accuracy and its fusion with audio features adds a slight improvement in these results.
Original languageEnglish
Pages960-964
Number of pages5
DOIs
Publication statusPublished - 15 Apr 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/TerritoryCanada
CityCalgary
Period15/04/1820/04/18

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