[1O5-GS-7] Vision, speech media processing

[1O5-GS-7-06] Accent discrimination from speech-imagery EEG.

Takuro Fukuda¹, Shun Sawada¹, Hidefumi Ohmura¹, 〇Kouichi Fukuda Katsurada¹, Yurie Iribe², Ryo Taguchi³, Tsuneo Nitta⁴ (1. Tokyo University of Science, 2. Aichi Prefectural University, 3. Nagoya Institute of Technology, 4. Toyohashi University of Technology)

[[Online]]

Keywords:Electroencephalogram, Speech-imagery, Accent discrimination, Convolutional Neural Network

Speech synthesis from neural decoding Although of analysis of speech- imagery recognition using electroencephalogram (EEG) could become a strong direct-communication tool in brain-computer interface (BCI). has been actively conducted. In this report, we propose a complex cepstrum-based accent discrimination from speech-imagery EEG signals. We first create a word-database with different accentuation that has hand-labeled short-syllables in imagined words after the pooling process of electrodes. created a database containing the intervals of imagined spoken syllables that is visually labeled from the line spectral patterns of EEG signals obtained after the pooling process of electrodes.created a database containing the intervals of imagined spoken syllables that is visually labeled from the line spectral patterns of EEG signals obtained after the pooling process of electrodes. Then, we design construct an accent discriminator based on using the the complex- cepstrum calculated from the amplitude- spectrum and phase-spectrum offrom the EEG signals during speech-imagery. The experiment of high and low pitch accents is conducted using three imagined words with same syllables but different accentuation. In the recognition stage, two approaches of SM and CNN are compared, and the eigenspaces are designedthe classifier based on CNN shows high performance.