Deep neural networks for anger detection from real life speech data

Jun Deng, Florian Eyben, Björn Schuller, Felix Burkhardt

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

There has been a lot of previous work on deep neural networks for automatic speech recognition, however, little emphasis has been placed on an investigation of effective deep learning architectures for anger detection from speech. In this paper, inspired by the state-of-the-art deep learning algorithms, we propose a variant of Deep Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs), Convolution Neural Networks (CNNs) with 3 × 3 kernels, and LSTM RNNs combined with CNNs, in conjunction with log-mel filter bank features and brute forced low-level-descriptors from the standardised ComParE set for speech anger detection. We extensively evaluate the deep networks on a big real-life speech corpus of 26 970 utterances with utterance-level labels collected from a German voice portal, finding that our proposed neural networks significantly outperform traditional modelling algorithms for speech anger detection.

Original languageEnglish
Title of host publication2017 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538606803
DOIs
StatePublished - 2 Jul 2017
Externally publishedYes
Event7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2017 - San Antonio, United States
Duration: 23 Oct 201726 Oct 2017

Publication series

Name2017 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2017
Volume2018-January

Conference

Conference7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2017
Country/TerritoryUnited States
CitySan Antonio
Period23/10/1726/10/17

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