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- W120054989 abstract "It is common knowledge that affective and emotion-related states are acoustically well modelled on a supra-segmental level. Nonetheless successes are reported for frame-level processing either by means of dynamic classification or multi-instance learning techniques. In this work a quantitative feature-type-wise comparison between frame-level and supra-segmental analysis is carried out for the recognition of interest in human conversational speech. To shed light on the respective differences the same classifier, namely Support-Vector-Machines, is used in both cases: once by clustering a ‘bag of frames’ of unknown sequence length employing Multi-Instance Learning techniques, and once by statistical functional application for the projection of the time series onto a static feature vector. As database serves the Audiovisual Interest Corpus of naturalistic interest. Index Terms: interest recognition, multi-instance learning, feature relevance" @default.
- W120054989 created "2016-06-24" @default.
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- W120054989 date "2009-09-06" @default.
- W120054989 modified "2023-09-25" @default.
- W120054989 title "Recognising interest in conversational speech - comparing bag of frames and supra-segmental features" @default.
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- W120054989 doi "https://doi.org/10.21437/interspeech.2009-484" @default.
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