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Speech emotion recognition based on long short-term memory and convolutional neural networks
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2018 (English)In: Journal of Nanjing University of Posts and Telecommunications, ISSN 1673-5439, Vol. 38, no 5, p. 63-69Article in journal (Refereed) Published
Abstract [en]

To improve the accuracy of speech emotion recognition, a speech emotion recognition method is proposed based on long short-term memory (LSTM) and convolutional neural network (CNN). Firstly, the Mel-frequency spectrum sequence of the speech signal is extracted, and then it is inputted into the LSTM network to extract the temporal context features of the speech signals. On this basis, CNN is used to extract high-level emotional features from low-level features and complete emotional classification of speech signals. The emotion recognition tests are carried out on eNTRAFACE'05, RML and AFEW6.0 databases. The experimental results show that the average recognition rates of the method on the above-mentioned three databases are 49.15%, 85.38% and 37.90%. In addition, compared with the traditional speech emotion recognition method and the speech emotion recognition method based on LSTM or CNN, the method has better recognition performance.

Place, publisher, year, edition, pages
Journal of Nanjing Institute of Posts and Telecommunications , 2018. Vol. 38, no 5, p. 63-69
Keywords [en]
Convolutional neural network(CNN), Human-computer interaction, Long short-term memory(LSTM), Speech emotion recognition
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-247152DOI: 10.14132/j.cnki.1673-5439.2018.05.009Scopus ID: 2-s2.0-85061187772OAI: oai:DiVA.org:kth-247152DiVA, id: diva2:1313982
Note

QC 20190507

Available from: 2019-05-07 Created: 2019-05-07 Last updated: 2019-05-07Bibliographically approved

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Citation style
  • apa
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf