Meridith Ringel
Julia Hirschberg
ABSTRACT
Navigating through new voicemall messages to find messages of interest is a time-consuming task, particularly for high-volume users. When checking messages under a time contraint (e.g., during a brief meeting break), users need to identify those messages requiring urgent action since not all messages can be processed in limited time. For these users, it would be useful if messages of greater urgency can be played first. For other users, distinguishing personal from business voicemail is a pressing need, to separate their home and business lives. We have successfully applied machine-learning techniques to lexical, acoustic, and contextual features of voicemail in order to sort messages based on urgency and on business-relevance.
- Freund, Y., et al. An Efficient Boosting Algorithm for Combing Preferences. Proceedings of Machine Learning, 1998. Google Scholar
Digital Library
- Hirschberg, J., et al. SCANMail: Browsing and Searching Speech Data by Content. Proceedings of EuroSpeech, 2001.Google Scholar
- Whittaker, S., et al. All Talk and All Action: Strategies for Managing Voicemail Messages. Proceedings of CHI, 1998. Google ScholarDigital Library
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