The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

This paper explores several statistical pattern recognition techniques to classify utterances according to their emotional content. We have recorded a corpus containing emotional speech with over a 1000 utterances from different speakers. We present a new method of extracting prosodic features from speech, based on a smoothing spline approximation of the pitch contour. To make maximal use of the limited amount of training data available, we introduce a novel pattern recognition technique: majority voting of subspace specialists. Using this technique, we obtain classification performance that is close to human performance on the task.

Notes

Text Reference

Frank Dellaert, , and , "Recognizing Emotion in Speech," Proceedings of the ICSLP '96, October, 1996.

BibTeX Reference

@inproceedings{Dellaert_1996_896,
   author = "Frank Dellaert and and ",
   title = "Recognizing Emotion in Speech",
   booktitle = "Proceedings of the ICSLP '96",
   month = "October",
   year = "1996",
}

The Robotics Institute

Menu