  The 2020 INTERSPEECH Computational Paralinguistics Challenge (ComParE)
consists of three Sub-Challenges, where the tasks are to identify the level of
arousal and valence of elderly speakers, determine whether the actual speaker
wearing a surgical mask, and estimate the actual breathing of the speaker. In
our contribution to the Challenge, we focus on the Elderly Emotion and the Mask
sub-challenges. Besides utilizing standard or close-to-standard features such
as ComParE functionals, Bag-of-Audio-Words and Fisher vectors, we exploit that
emotion is related to the velocity of speech (i.e. speech rate). To utilize
this, we perform phone-level recognition using an ASR system, and extract
features from the output such as articulation tempo, speech tempo, and various
attributes measuring the amount of pauses. We also hypothesize that wearing a
surgical mask makes the speaker feel uneasy, leading to a slower speech rate
and more hesitations; hence, we experiment with the same features in the Mask
sub-challenge as well. Although this theory was not justified by the
experimental results on the Mask Sub-Challenge, in the Elderly Emotion
Sub-Challenge we got significantly improved arousal and valence values with
this feature type both on the development set and in cross-validation.
