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.
|Number of pages||5|
|Publication status||Published - 15 Apr 2018|
|Event||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada|
Duration: 15 Apr 2018 → 20 Apr 2018
|Conference||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018|
|Period||15/04/18 → 20/04/18|