Transfer learning for improving speech emotion classification accuracy

Paper

Latif, Siddique, Rana, Rajib, Younis, Shahzad, Qadir, Junaid and Epps, Julien. 2018. "Transfer learning for improving speech emotion classification accuracy." 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.2018-1625
Paper/Presentation Title

Transfer learning for improving speech emotion classification accuracy

Presentation TypePaper
AuthorsLatif, Siddique (Author), Rana, Rajib (Author), Younis, Shahzad (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.2018-1625
Web Address (URL) of Paperhttps://www.isca-speech.org/archive/interspeech_2018/latif18b_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

The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from the same corpus collected under the same conditions. The performance of such systems has been shown to drop significantly in cross-corpus and cross-language scenarios. To address the problem, this paper exploits a transfer learning technique to improve the performance of speech emotion recognition systems that are novel in cross-language and cross-corpus scenarios. Evaluations on five different corpora in three different languages show that Deep Belief Networks (DBNs) offer better accuracy than previous approaches on cross-corpus emotion recognition, relative to a Sparse Autoencoder and Support Vector Machine (SVM) baseline system. Results also suggest that using a large number of languages for training and using a small fraction of the target data in training can significantly boost accuracy compared with baseline also for the corpus with limited training examples.

Keywordstransfer learning, cross-corpus, deep belief networks, sparse autoencoder, support vector machine
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460212. Speech recognition
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Byline AffiliationsInformation Technology University, Pakistan
Institute for Resilient Regions
National University of Sciences and Technology, Pakistan
University of New South Wales
Institution of OriginUniversity of Southern Queensland
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