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In this paper, we propose a novel method for highly efficient exploitation of unlabeled data—Cooperative Learning. Our approach consists of combining Active Learning and Semi-Supervised Learning techniques, with the aim of reducing the costly effects of human annotation. The core underlying idea of Cooperative Learning is to share the labeling work between human and machine efficiently in such a way...
In speech communication, humans are able to perceive not only the acoustic features but also the articulation state from speech sounds. Some specific articulation configurations can carry certain non-linguistic information such as emotion. However, the relation between articulation and acoustic features is still not clear. To clarify such a relation, we analyzed speech data from the emotions of cold...
Anger detection in speech-based automated telephone applications is a growing field of research. In this work we report on inter-labeler agreement in a “real-life” anger detection task for Interactive Voice Response (IVR) systems. The presented study is based on a corpus of 1.911 calls containing 22.711 utterances and describes considerations prior to the rating process. We point out difficulties...
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