Automatic detection of speaker state: Lexical,prosodic, and phonetic approaches to level-of-interest and intoxication classification |
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Authors: | William Yang Wang Fadi Biadsy Andrew Rosenberg Julia Hirschberg |
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Affiliation: | 1. Department of Computer Science, Columbia University, United States;2. Computer Science Department, Queens College (CUNY), United States |
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Abstract: | Traditional studies of speaker state focus primarily upon one-stage classification techniques using standard acoustic features. In this article, we investigate multiple novel features and approaches to two recent tasks in speaker state detection: level-of-interest (LOI) detection and intoxication detection. In the task of LOI prediction, we propose a novel Discriminative TFIDF feature to capture important lexical information and a novel Prosodic Event detection approach using AuToBI; we combine these with acoustic features for this task using a new multilevel multistream prediction feedback and similarity-based hierarchical fusion learning approach. Our experimental results outperform published results of all systems in the 2010 Interspeech Paralinguistic Challenge – Affect Subchallenge. In the intoxication detection task, we evaluate the performance of Prosodic Event-based, phone duration-based, phonotactic, and phonetic-spectral based approaches, finding that a combination of the phonotactic and phonetic-spectral approaches achieve significant improvement over the 2011 Interspeech Speaker State Challenge – Intoxication Subchallenge baseline. We discuss our results using these new features and approaches and their implications for future research. |
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