Automatically augmenting an emotion dataset improves classification using audio

@inproceedings{Lakomkin2017AutomaticallyAA,
  title={Automatically augmenting an emotion dataset improves classification using audio},
  author={Egor Lakomkin and Cornelius Weber and Stefan Wermter},
  booktitle={EACL},
  year={2017}
}
  • Egor Lakomkin, Cornelius Weber, Stefan Wermter
  • Published in EACL 2017
  • Computer Science
  • In this work, we tackle a problem of speech emotion classification. One of the issues in the area of affective computation is that the amount of annotated data is very limited. On the other hand, the number of ways that the same emotion can be expressed verbally is enormous due to variability between speakers. This is one of the factors that limits performance and generalization. We propose a simple method that extracts audio samples from movies using textual sentiment analysis. As a result, it… CONTINUE READING

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