Static and Dynamic Modelling for the Recognition of Non-verbal Vocalisations in Conversational Speech

Non-verbal vocalisations such as laughter, breathing, hesitation, and consent play an important role in the recognition and understanding of human conversational speech and spontaneous affect. In this contribution we discuss two different strategies for robust discrimination of such events: dynamic modelling by a broad selection of diverse acoustic Low-Level-Descriptors vs. static modelling by projection of these via statistical functionals onto a 0.6k feature space with subsequent de-correlation. As classifiers we employ Hidden Markov Models, Conditional Random Fields, and Support Vector Machines, respectively. For discussion of extensive parameter optimisation test-runs with respect to features and model topology, 2.9k non-verbals are extracted from the spontaneous Audio-Visual Interest Corpus. 80.7% accuracy can be reported with, and 92.6% without a garbage model for the discrimination of the named classes.