An acoustic and lexical analysis of emotional valence in spontaneous speech: Autobiographical memory recall in older adults

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.