Direct modelling of speech emotion from raw speech

Paper

Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja and Epps, Julien. 2019. "Direct modelling of speech emotion from raw speech." 20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language (INTERSPEECH 2019). Graz, Austria 15 - 19 Sep 2019 France. https://doi.org/10.21437/Interspeech.2019-3252
Paper/Presentation Title

Direct modelling of speech emotion from raw speech

Presentation TypePaper
AuthorsLatif, Siddique (Author), Rana, Rajib (Author), Khalifa, Sara (Author), Jurdak, Raja (Author) and Epps, Julien (Author)
Journal or Proceedings TitleProceedings of the 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019)
Number of Pages5
Year2019
Place of PublicationFrance
ISBN9781510896833
Digital Object Identifier (DOI)https://doi.org/10.21437/Interspeech.2019-3252
Web Address (URL) of Paperhttps://www.isca-speech.org/archive/interspeech_2019/latif19_interspeech.html
Conference/Event20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language (INTERSPEECH 2019)
Event Details
20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language (INTERSPEECH 2019)
Event Date
15 to end of 19 Sep 2019
Event Location
Graz, Austria
Abstract

Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. However, a filter bank that is designed from perceptual evidence is not always guaranteed to be the best in a statistical modelling framework where the end goal is for example emotion classification. This has fuelled the emerging trend of learning representations from raw speech especially using deep learning neural networks. In particular, a combination of Convolution Neural Networks (CNNs) and Long Short Term Memory (LSTM) have gained great traction for the intrinsic property of LSTM in learning contextual information crucial for emotion recognition; and CNNs been used for its ability to overcome the scalability problem of regular neural networks. In this paper, we show that there are still opportunities to improve the performance of emotion recognition from the raw speech by exploiting the properties of CNN in modelling contextual information. We propose the use of parallel convolutional layers to harness multiple temporal resolutions in the feature extraction block that is jointly trained with the LSTM based classification network for the emotion recognition task. Our results suggest that the proposed model can reach the performance of CNN trained with hand-engineered features from both IEMOCAP and MSP-IMPROV datasets.

Keywordsspeech emotion, raw speech, convolutional neural networks, long short term memory
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460212. Speech recognition
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Byline AffiliationsInstitute for Resilient Regions
University of New South Wales
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
University of New South Wales
Institution of OriginUniversity of Southern Queensland
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