Abstract
Analyzing emotional valence in spontaneous speech remains complex and challenging. We present an acoustic and lexical analysis of emotional valence in spontaneous speech of older adults. Data was collected by recalling autobiographical memories through a word association task. Due to the complex and personal nature of memories, we propose a novel coding scheme for emotional valence. We explore acoustic properties of speech as well as the use of affective words to predict emotional valence expressed in autobiographical memories. Using mixed-effect regression modelling, we compared predictive models based on acoustic information only, lexical information only, or a combination of both. Results show that the combined model accounts for the highest proportion of explained variance, with the acoustic features accounting for a smaller share of the total variance than the lexical features. Several acoustic and lexical features predicted valence. As a first attempt at analyzing spontaneous emotional speech in older adults autobiographical memories, the study provides more insight in which acoustic features can be used to predict valence (automatically) in a more ecologically valid setting.
Original language | English |
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Title of host publication | Proceedings Interspeech 2019 |
Publisher | International Speech Communication Association |
Pages | 3287-3291 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Event | 20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language, INTERSPEECH 2019 - Messecongress Graz, Graz, Austria Duration: 15 Sept 2019 → 19 Sept 2019 Conference number: 20 https://www.interspeech2019.org/ |
Conference
Conference | 20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language, INTERSPEECH 2019 |
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Abbreviated title | INTERSPEECH 2019 |
Country/Territory | Austria |
City | Graz |
Period | 15/09/19 → 19/09/19 |
Internet address |
Keywords
- Autobiographical memory recall
- Life events
- Older adults
- Sentiment analysis
- Speech analysis
- Valence