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The goal of this investigation was the assessment of acoustic infant vocalizations by laypersons. More specifically, the goal was to identify (1) the set of most salient classes for infant vocalizations, (2) their relationship to each other and to affective ratings, and (3) proposals for classification schemes based on these labels and relationships. The assessment behavior of laypersons has not yet been investigated, as current infant vocalization classification schemes have been aimed at professional and scientific applications. The study methodology was based on the Nijmegen protocol, in which participants rated vocalization recordings regarding acoustic class labels, and continuous affective scales valence, tense arousal and energetic arousal. We determined consensus stimuli ratings as well as stimuli similarities based on participant ratings. Our main findings are: (1) we identified 9 salient labels, (2) valence has the overall greatest association to label ratings, (3) there is a strong association between label and valence ratings in the negative valence space, but low association for neutral labels, and (4) stimuli separability is highest when grouping labels into 3 - 5 classes. We finally propose two classification schemes based on these findings.
It has been suggested in developmental psychology literature that the communication of affect between mothers and their infants correlates with the socioemotional and cognitive development of infants. In this study, we obtained day-long audio recordi
Mice vocalize in the ultrasonic range during social interactions. These vocalizations are used in neuroscience and clinical studies to tap into complex behaviors and states. The analysis of these ultrasonic vocalizations (USVs) has been traditionally
We design a framework for studying prelinguistic child voicefrom 3 to 24 months based on state-of-the-art algorithms in di-arization. Our system consists of a time-invariant feature ex-tractor, a context-dependent embedding generator, and a clas-sifi
In the domain of social signal processing, audio event detection is a promising avenue for accessing daily behaviors that contribute to health and well-being. However, despite advances in mobile computing and machine learning, audio behavior detectio
Most of the pronunciation assessment methods are based on local features derived from automatic speech recognition (ASR), e.g., the Goodness of Pronunciation (GOP) score. In this paper, we investigate an ASR-free scoring approach that is derived from