Oliver Bodemer
IBM J. Res. Dev
We have applied speech recognition and text-mining technologies to a set of 522 recorded outbound marketing calls and analyzed the results. Since speaker-independent speech recognition technology results in a significantly lower recognition rate than that found when the recognizer is trained for a particular speaker, we applied a number of post-processing algorithms to the output of the recognizer to render it suitable for the Textract text mining system. We indexed the call transcripts using a search engine and used Textract and associated Java technologies to place the relevant terms for each document in a relational database. Following a search query, we generated a thumbnail display of the results of each call with the salient terms highlighted. We illustrate these results and discuss their utility. We describe a distinct document genre based on the note-taking concept of document content, and propose a significant new method for measuring speech recognition accuracy.
Oliver Bodemer
IBM J. Res. Dev
Frank R. Libsch, Takatoshi Tsujimura
Active Matrix Liquid Crystal Displays Technology and Applications 1997
Limin Hu
IEEE/ACM Transactions on Networking
Elena Cabrio, Philipp Cimiano, et al.
CLEF 2013