Work

Emotion recognition from speech: Putting ASR in the loop

Year: 2009

Type: conference-paper

Abstract: This paper investigates the automatic recognition of emotion from spoken words by vector space modeling vs. string kernels which have not been investigated in this respect, yet. Apart from the spoken content directly, we integrate part-of-speech and higher semantic tagging in our analyses. As opposed to most works in the field, we evaluate the performance with an ASR engine in the loop. Extensive experiments are run on the FAU Aibo Emotion Corpus of 4 k spontaneous emotional child-robot interactions and show surprisingly low performance degradation with real ASR over transcription-based emotion recognition. In the result, bag of words dominate over all other modeling forms based on the spoken content.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp.2009.4960651

Authors: Bjorn Schuller, Anton Batliner, Stefan Steidl, Dino Seppi

Institutions: Technical University of Munich, Friedrich-Alexander-Universität Erlangen-Nürnberg

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funder: European Commission

Award: Sustained Emotionally coloured Machine-human Interaction using Nonverbal Expression