Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88357
Title: End-to-End Speech Emotion Recognition Using Multi-Scale Convolution Networks
Authors: Sivanagaraja, Tatinati
Ho, Mun Kit
Khong, Andy Wai Hoong
Wang, Yubo
Keywords: Machine Learning
Emotion Recognition
Issue Date: 2017
Source: Sivanagaraja, T., Ho, M. K., Khong, A. W. H., & Wang, Y. (2017). End-to-End Speech Emotion Recognition Using Multi-Scale Convolution Networks. Paper presented at 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia (pp. 189-192).
Abstract: Automatic speech emotion recognition is one of the challenging tasks in machine learning community mainly due to the significant variations across individuals while expressing the same emotion cue. The success of emotion recognition with machine learning techniques primarily depends on the feature set chosen to learn. Formulation of appropriate features that cater for all variations in emotion cues however is not a trivial task. Recent works on emotion recognition with deep learning techniques thus focus on the end-to-end learning scheme which identifies the features directly from the raw speech signal instead of relying on hand-crafted feature set. Existing methods in this scheme however did not take into account the fact that speech signals often exhibit distinct features at different time scales and frequencies than in the raw form. We propose the multi- scale convolution neural network (MCNN) to identify features at different time scales and frequencies from raw speech signals. This end-to-end model leverages on the multi-branch input layer and tunable convolution layers to learn the identified features which are subsequently employed to recognize the emotion cues accordingly. As a proof-of-concept, the MCNN method with a fixed transformation stage is evaluated using the SAVEE emotion database. Results showed that MCNN improves the emotion recognition performance when compared to existing methods, which underpins the necessity of learning features at different time scales.
URI: https://hdl.handle.net/10356/88357
http://hdl.handle.net/10220/44716
DOI: 10.1109/APSIPA.2017.8282026
Rights: © 2017 IEEE. 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. The published version is available at: [http://dx.doi.org/10.1109/APSIPA.2017.8282026].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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