Variational Autoencoders to Learn Latent Representations of Speech Emotion

Paper

Latif, Siddique, Rana, Rajib, Qadir, Junaid and Epps, Julien. 2018. "Variational Autoencoders to Learn Latent Representations of Speech Emotion." 19th Annual Conference of the International Speech Communication Association: Speech Research for Emerging Markets in Multilingual Societies (INTERSPEECH 2018). Hyderabad, India 02 - 06 Sep 2018 France. https://doi.org/10.21437/Interspeech.2018-1568
Paper/Presentation Title

Variational Autoencoders to Learn Latent Representations of Speech Emotion

Presentation TypePaper
AuthorsLatif, Siddique (Author), Rana, Rajib (Author), Qadir, Junaid (Author) and Epps, Julien (Author)
Journal or Proceedings TitleProceedings of the 19th Annual Conference of the International Speech Communication Association (INTERSPEECH 2018)
Number of Pages5
Year2018
Place of PublicationFrance
Digital Object Identifier (DOI)https://doi.org/10.21437/Interspeech.2018-1568
Web Address (URL) of Paperhttps://www.isca-speech.org/archive/interspeech_2018/latif18_interspeech.html
Conference/Event19th Annual Conference of the International Speech Communication Association: Speech Research for Emerging Markets in Multilingual Societies (INTERSPEECH 2018)
Event Details
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A
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Event Details
19th Annual Conference of the International Speech Communication Association: Speech Research for Emerging Markets in Multilingual Societies (INTERSPEECH 2018)
Event Date
02 to end of 06 Sep 2018
Event Location
Hyderabad, India
Abstract

Learning the latent representation of data in unsupervised fashion is a very interesting process that provides relevant features for enhancing the performance of a classifier. For speech emotion recognition tasks, generating effective features is crucial. Currently, handcrafted features are mostly used for speech emotion recognition, however, features learned automatically using deep learning have shown strong success in many problems, especially in image processing. In particular, deep generative models such as Variational Autoencoders (VAEs) have gained enormous success in generating features for natural images. Inspired by this, we propose VAEs for deriving the latent representation of speech signals and use this representation to classify emotions. To the best of our knowledge, we are the first to propose VAEs for speech emotion classification. Evaluations on the IEMOCAP dataset demonstrate that features learned by VAEs can produce state-of-the-art results for speech emotion classification.

Keywordsspeech emotion classification, variational auto-encoders, deep learning, feature learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460212. Speech recognition
461106. Semi- and unsupervised learning
461104. Neural networks
461103. Deep learning
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Byline AffiliationsInformation Technology University, Pakistan
Institute for Resilient Regions
University of New South Wales
Institution of OriginUniversity of Southern Queensland
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