Automatic recognition of speaker physical load using posterior probability based features from acoustic and phonetic tokens
Ming Li · 2014
This paper presents an automatic speaker physical load recog-nition approach using posterior probability based features from acoustic and phonetic tokens. In this method, the tokens for calculating the posterior probability or zero-order statistics are extended from the conventional MFCC trained Gaussian Mix-ture Models (GMM) components to parallel phonetic phonemes and tandem feature trained GMM components. Phoneme rec-ognizers from five different languages are employed to extract the phoneme posterior probabilities. We show that these his-togram style features at both the acoustic and phonetic levels are effective and complementary for capturing the speaker phys-ical load information from short utterances. Support vector ma-chine is adopted as the supervised classifier. By combining the proposed methods with the OpenSMILE baseline which covers the acoustic and prosodic information further improves the fi-nal performance. The proposed fusion system achieves 70.18% and 72.81 % unweighted accuracy on the validation and test set of the Munich Bio-voice Corpus for the binary physical load level recognition task in the INTERSPEECH 2014 Computa-tional Paralinguistics Challenge. Index Terms: physical load sub-challenge, speaker physical load recognition, posterior probability features