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        <title>ASR-based Features for Emotion Recognition: A Transfer Learning Approach</title>
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        <namePart type="given">Noé</namePart>
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    <abstract>During the last decade, the applications of signal processing have drastically improved with deep learning. However areas of affecting computing such as emotional speech synthesis or emotion recognition from spoken language remains challenging. In this paper, we investigate the use of a neural Automatic Speech Recognition (ASR) as a feature extractor for emotion recognition. We show that these features outperform the eGeMAPS feature set to predict the valence and arousal emotional dimensions, which means that the audio-to-text mapping learned by the ASR system contains information related to the emotional dimensions in spontaneous speech. We also examine the relationship between first layers (closer to speech) and last layers (closer to text) of the ASR and valence/arousal.</abstract>
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    <identifier type="doi">10.18653/v1/W18-3307</identifier>
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        <date>2018-jul</date>
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%0 Conference Proceedings
%T ASR-based Features for Emotion Recognition: A Transfer Learning Approach
%A Tits, Noé
%A El Haddad, Kevin
%A Dutoit, Thierry
%S Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML)
%D 2018
%8 jul
%I Association for Computational Linguistics
%C Melbourne, Australia
%F tits-etal-2018-asr
%X During the last decade, the applications of signal processing have drastically improved with deep learning. However areas of affecting computing such as emotional speech synthesis or emotion recognition from spoken language remains challenging. In this paper, we investigate the use of a neural Automatic Speech Recognition (ASR) as a feature extractor for emotion recognition. We show that these features outperform the eGeMAPS feature set to predict the valence and arousal emotional dimensions, which means that the audio-to-text mapping learned by the ASR system contains information related to the emotional dimensions in spontaneous speech. We also examine the relationship between first layers (closer to speech) and last layers (closer to text) of the ASR and valence/arousal.
%R 10.18653/v1/W18-3307
%U https://aclanthology.org/W18-3307
%U https://doi.org/10.18653/v1/W18-3307
%P 48-52

Markdown (Informal)

[ASR-based Features for Emotion Recognition: A Transfer Learning Approach](https://aclanthology.org/W18-3307) (Tits et al., 2018)

• ASR-based Features for Emotion Recognition: A Transfer Learning Approach (Tits et al., 2018)

ACL

• Noé Tits, Kevin El Haddad, and Thierry Dutoit. 2018. ASR-based Features for Emotion Recognition: A Transfer Learning Approach. In Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML), pages 48–52, Melbourne, Australia. Association for Computational Linguistics.