JAIST Repository >
School of Information Science >
Conference Papers >
Conference Papers >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18192

Title: Speech Emotion Recognition Using Multichannel Parallel Convolutional Recurrent Neural Networks based on Gammatone Auditory Filterbank
Authors: Peng, Zhichao
Zhu, Zhi
Unoki, Masashi
Dang, Jianwu
Akagi, Masato
Issue Date: 2017-12-15
Publisher: APSIPA
Magazine name: Proceedings of APSIPA Annual Summit and Conference 2017
Abstract: Speech Emotion Recognition (SER) using deep learning methods based on computational auditory models of human auditory system is a new way to identify emotional state. In this paper, we propose to utilize multichannel parallel convolutional recurrent neural networks (MPCRNN) to extract salient features based on Gammatone auditory filterbank from raw waveform and reveal that this method is effective for speech emotion recognition. We first divide the speech signal into segments, and then get multichannel data using Gammatone auditory filterbank, which is used as a first stage before applying MPCRNN to get the most relevant features for emotion recognition from speech. We subsequently obtain emotion state probability distribution for each speech segment. Eventually, utterance-level features are constructed from segment-level probability distributions and fed into support vector machine (SVM) to identify the emotions. According to the experimental results, speech emotion features can be effectively learned utilizing the proposed deep learning approach based on Gammatone auditory filterbank.
Rights: Copyright (C) 2017 APSIPA. This material is posted here with permission of APSIPA (Asia-Pacific Signal and Information Processing Association). Zhichao Peng, Zhi Zhu, Masashi Unoki, Jianwu Dang, Masato Akagi, Proceedings of APSIPA Annual Summit and Conference 2017
URI: http://hdl.handle.net/10119/18192
Material Type: publisher
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

Files in This Item:

File Description SizeFormat
APSIPA2017.pdf569KbAdobe PDFView/Open

All items in DSpace are protected by copyright, with all rights reserved.


Contact : Library Information Section, Japan Advanced Institute of Science and Technology