Accent discrimination from speech-imagery EEG.

Abstract

Although analysis of speech imagery electroencephalogram (EEG) has been actively conducted, there have been reported few numbers of results that focus on pitch accent, which is a linguistic feature of imagined speech. In this report, we propose a complex cepstrum-based accent discrimination from speech-imagery EEG signals. We first 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 construct an accent discriminator using the complex cepstrum calculated from the amplitude spectrum from the EEG signals during speech-imagery. In the discrimination process, the eigenspaces are designed for each accent from the training data. The results of experiments using the subspace method and the tensor product-based compound similarity method showed satisfactory scores in discriminating the different types of accents of imagined two-syllable speeches.