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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/15481

Title: Auditory-Inspired End-to-End Speech Emotion Recognition Using 3D Convolutional Recurrent Neural Networks Based on Spectral-Temporal Representation
Authors: Peng, Zhichao
Zhu, Zhi
Unoki, Masashi
Dang, Jianwu
Akagi, Masato
Keywords: temporal modulation
three-dimensional convolutional recurrent neural networks
spectral-temporal representation
speech emotion recognition
Issue Date: 2018-07-26
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2018 IEEE International Conference on Multimedia and Expo (ICME)
Start page: 1
End page: 6
DOI: 10.1109/ICME.2018.8486564
Abstract: The human auditory system has far superior emotion recognition abilities compared with recent speech emotion recognition systems, so research has focused on designing emotion recognition systems by mimicking the human auditory system. Psychoacoustic and physiological studies indicate that the human auditory system decomposes speech signals into acoustic and modulation frequency components, and further extracts temporal modulation cues. Speech emotional states are perceived from temporal modulation cues using the spectral and temporal receptive field of the neuron. This paper proposes an emotion recognition system in an end-to-end manner using three-dimensional convolutional recurrent neural networks (3D-CRNNs) based on temporal modulation cues. Temporal modulation cues contain four-dimensional spectral-temporal (ST) integration representations directly as the input of 3D-CRNNs. The convolutional layer is used to extract high-level multiscale ST representations, and the recurrent layer is used to extract long-term dependency for emotion recognition. The proposed method was verified on the IEMOCAP database. The results show that our proposed method can exceed the recognition accuracy compared to that of the state-of-the-art systems.
Rights: This is the author's version of the work. Copyright (C) 2018 IEEE. 2018 IEEE International Conference on Multimedia and Expo (ICME), 2018, DOI:10.1109/ICME.2018.8486564. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/15481
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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