Year: 2025
Type: preprint
Abstract: The same speech content produced by different speakers exhibits significant differences in pitch contour, yet listeners' semantic perception remains unaffected. This phenomenon may stem from the brain's perception of pitch contours being independent of individual speakers' pitch ranges. In this work, we recorded electroencephalogram (EEG) while participants listened to Mandarin monosyllables with varying tones, phonemes, and speakers. The CE-ViViT model is proposed to decode raw or speaker-normalized pitch contours directly from EEG. Experimental results demonstrate that the proposed model can decode pitch contours with modest errors, achieving performance comparable to state-of-the-art EEG regression methods. Moreover, speaker-normalized pitch contours were decoded more accurately, supporting the neural encoding of relative pitch.
Source: arXiv (Cornell University)
Authors: Chen, Jiaxin, Wang, Yiming, Zhang, Ziyu, Han, Jiayang, Liu, Yin-Long, Feng, Rui, Liang, Xiuyuan, Ling, Zhen-Hua, Yuan, Jiahong
Institution: University of Science and Technology of China
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979310465
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