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Detecting Levels of Interest from Spoken Dialog with Multistream Prediction Feedback and Similarity Based Hierarchical Fusion Learning

Wang, William Yang; Hirschberg, Julia Bell

Detecting levels of interest from speakers is a new problem in Spoken Dialog Understanding with significant impact on real world business applications. Previous work has focused on the analysis of traditional acoustic signals and shallow lexical features. In this paper, we present a novel hierarchical fusion learning model that takes feedback from previous multistream predictions of prominent seed samples into account and uses a mean cosine similarity measure to learn rules that improve reclassification. Our method is domain-independent and can be adapted to
other speech and language processing areas where domain adaptation is expensive to perform. Incorporating Discriminative Term Frequency
and Inverse Document Frequency (DTFIDF), lexical affect scoring, and low and high level prosodic and acoustic features, our experiments outperform the published results of all systems participating in the 2010 Interspeech Paralinguistic Affect Subchallenge.

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More About This Work

Academic Units
Computer Science
Publisher
Proceedings of SIGDIAL 2011
Published Here
August 2, 2013
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