Fusing Acoustic Feature Representations for Computational Paralinguistics Tasks

17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016) -- SEP 08-12, 2016 -- San Francisco, CAThe field of Computational Paralinguistics is rapidly growing and is of interest in various application domains ranging from biomedical engineering to forensics. The INTERSPEECH ComParE challenge series has a field-leading role, introducing novel problems with a common benchmark protocol for comparability. In this work, we tackle all three ComParE 2016 Challenge corpora (Native Language, Sincerity and Deception) benefiting from multi-level normalization on features followed by fast and robust kernel learning methods. Moreover, we employ computer vision inspired low level descriptor representation methods such as the Fisher vector encoding. After nonlinear preprocessing, obtained Fisher vectors are kernelized and mapped to target variables by classifiers based on Kernel Extreme Learning Machines and Partial Least Squares regression. We finally combine predictions of models trained on popularly used functional based descriptor encoding (openSMILE features) with those obtained from the Fisher vector encoding. In the preliminary experiments, our approach has significantly outperformed the baseline systems for Native Language and Sincerity sub-challenges both in the development and test sets.apple, amazon alexa, Google, Microsoft, ebay, facebook, YAHOO JAPAN, Baidu Res, IBM Res, CIRRUS LOGIC, DATATANG, NUANCE, Speechocean Ltd, Yandex, Raytheon TechnolRussian Foundation for Basic ResearchRussian Foundation for Basic Research (RFBR) [16-37-60100]This research is financially supported by the Russian Foundation for Basic Research (project Ns 16-37-60100)