Segmenting into Adequate Units for Automatic Recognition of Emotion-Related Episodes: A Speech-Based Approach | Zendy

Anton Batliner | Zendy; Dino Seppi | Zendy; Stefan Steidl | Zendy; Björn W. Schuller | Zendy

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Segmenting into Adequate Units for Automatic Recognition of Emotion-Related Episodes: A Speech-Based Approach

Author(s) -

Anton Batliner,

Dino Seppi,

Stefan Steidl,

Björn W. Schuller

Publication year - 2010

Publication title -

advances in human-computer interaction

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.429

H-Index - 21

eISSN - 1687-5907

pISSN - 1687-5893

DOI - 10.1155/2010/782802

Subject(s) - paralanguage , computer science , natural language processing , emotion classification , speech recognition , valence (chemistry) , part of speech , emotional valence , market segmentation , word (group theory) , artificial intelligence , class (philosophy) , segmentation , psychology , linguistics , cognition , communication , philosophy , physics , quantum mechanics , marketing , neuroscience , business

We deal with the topic of segmenting emotion-related (emotional/affective) episodes into adequate units for analysis and automatic processing/classification—a topic that has not been addressed adequately so far. We concentrate on speech and illustrate promising approaches by using a database with children's emotional speech. We argue in favour of the word as basic unit and map sequences of words on both syntactic and ‘‘emotionally consistent” chunks and report classification performances for an exhaustive modelling of our data by mapping word-based paralinguistic emotion labels onto three classes representing valence (positive, neutral, negative), and onto a fourth rest (garbage) class

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