We investigate classification of non-linguistic vocalisations with a novel audiovisual approach and Long Short-Term Memory (LSTM) Recurrent Neural Networks as highly successful dynamic sequence classifiers. As database of evaluation serves this year's Paralinguistic Challenge's Audiovisual Interest Corpus of human-to-human natural conversation. For video-based analysis we compare shape and appearance based features. These are fused in an early manner with typical audio descriptors. The results show significant improvements of LSTM networks over a static approach based on Support Vector Machines. More important, we can show a significant gain in performance when fusing audio and visual shape features.
Eyben et al. REF used it for audiovisual classification of vocal outbursts in human conversation and the results showed significant improvements over a static approach based on SVM.
Audiovisual classification of vocal outbursts in human conversation using Long-Short-Term Memory networks
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science Engineering