Only a few studies exist on automatic emotion analysis of speech from children with Autism Spectrum Conditions (ASC). Out of these, some preliminary studies have recently focused on comparing the relevance of selected acoustic features against large sets of prosodic, spectral, and cepstral features; however, no study so far provided a comparison of performances across different languages. The present contribution aims to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases of prompted phrases collected in English, Swedish, and Hebrew, inducing nine emotion categories embedded in short-stories. The datasets contain speech of children with ASC and typically developing children under the same conditions. We evaluate automatic diagnosis and recognition of emotions in atypical children's voice over the nine categories including binary valence/arousal discrimination.
|Number of pages||5|
|Journal||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|State||Published - 2015|
|Event||16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, Germany|
Duration: 6 Sep 2015 → 10 Sep 2015
Bibliographical notePublisher Copyright:
Copyright © 2015 ISCA.
- Autism Spectrum Conditions
- Emotion Recognition
- Feature Analysis
- Knowledge Based Systems
- Speech Classification