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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/15512

Title: A Three-Layer Emotion Perception Model for Valence and Arousal-Based Detection from Multilingual Speech
Authors: Li, Xingfeng
Akagi, Masato
Keywords: emotion recognition
emotion dimension
threelayer model
prosodic feature
spectrogram
glottal waveform
Issue Date: 2018
Publisher: International Speech Communication Association
Magazine name: Proc. Interspeech 2018
Start page: 3643
End page: 3647
DOI: 10.21437/Interspeech.2018-1820
Abstract: Automated emotion detection from speech has recently shifted from monolingual to multilingual tasks for human-like interaction in real-life where a system can handle more than a single input language. However, most work on monolingual emotion detection is difficult to generalize in multiple languages, because the optimal feature sets of the work differ from one language to another. Our study proposes a framework to design, implement and validate an emotion detection system using multiple corpora. A continuous dimensional space of valence and arousal is first used to describe the emotions. A three-layer model incorporated with fuzzy inference systems is then used to estimate two dimensions. Speech features derived from prosodic, spectral and glottal waveform are examined and selected to capture emotional cues. The results of this new system outperformed the existing state-of-the-art system by yielding a smaller mean absolute error and higher correlation between estimates and human evaluators. Moreover, results for speaker independent validation are comparable to human evaluators.
URI: http://hdl.handle.net/10119/15512
Material Type: publisher
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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