Cross-Corpus Experiments on Laughter and Emotion Detection in HRI with Elderly People

摘要

Social Signal Processing such as laughter or emotion detection is a very important issue, particularly in the field of human-robot interaction (HRI). At the moment, very few studies exist on elderly-people’s voices and social markers in real-life HRI situations. This paper presents a cross-corpus study with two realistic corpora featuring elderly people (ROMEO2 and ARMEN) and two corpora collected in laboratory conditions with young adults (JEMO and OFFICE). The goal of this experiment is to assess how good data from one given corpus can be used as a training set for another corpus, with a specific focus on elderly people voices. First, clear differences between elderly people real-life data and young adults laboratory data are shown on acoustic feature distributions (such as $$F_0$$ standard deviation or local jitter). Second, cross-corpus emotion recognition experiments show that elderly people real-life corpora are much more complex than laboratory corpora. Surprisingly, modeling emotions with an elderly people corpus do not generalize to another elderly people corpus collected in the same acoustic conditions but with different speakers. Our last result is that laboratory laughter is quite homogeneous across corpora but this is not the case for elderly people real-life laughter.