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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >On-Talk and Off-Talk Detection: A Discrete Wavelet Transform Analysis of Electroencephalogram
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On-Talk and Off-Talk Detection: A Discrete Wavelet Transform Analysis of Electroencephalogram

机译：谈话和脱谈检测：脑电图的离散小波变换分析

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摘要

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.
机译：与机器的口头互动导致面对面人类通信中不是很常见的行为：off-talk，它被定义为没有针对立即对话者，机器的语音话语，而是给另一个人或者 即便是自己。 我们的论点是，能够检测到脱谈话语的系统可以通过承认话语没有针对系统并因此而不是回复脱谈话语来以更有效的方式与人类交互。 在本文中，我们展示了使用小波变换分析的宽范围的脑电图（EEG）频带的辨别力，并使用音频和脑电图信号提出用于谈话和脱谈检测的模型及其融合。 我们的研究表明，EEG信号可以识别具有有希望的准确性的脱谈话题的发生，并且其与音频功能的融合增加了这些结果的略微改善。

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