TY - GEN
T1 - Static and dynamic modelling for the recognition of non-verbal vocalisations in conversational speech
AU - Schuller, Björn
AU - Eyben, Florian
AU - Rigoll, Gerhard
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=48249106592&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69369-7_12
DO - 10.1007/978-3-540-69369-7_12
M3 - Conference contribution
AN - SCOPUS:48249106592
SN - 3540693688
SN - 9783540693680
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 110
BT - Perception in Multimodal Dialogue Systems - 4th IEEE Tutorial and Research Workshop on Perception and Interactive Technologies for Speech-Based Systems, PIT 2008, Proceedings
T2 - 4th IEEE Tutorial and Research Workshop on Perception and Interactive Technologies for Multimodal Dialogue Systems, PIT 2008
Y2 - 16 June 2008 through 18 June 2008
ER -