Call Analysis with Classification Using Speech and Non-Speech Features

This paper reports our recent development of a highly reliable call analysis technique that makes novel use of automatic speech recognition (ASR), speech utterance classification and non-speech features. The main ideas include the use the N-Gram filler model to improve the ASR accuracy on important words in a message, and the integration of recognized utterance with non-speech features such as utterance length, and the use of utterance classification technique to interpret the message and extract additional information. Experimental evaluation shows that the use of the utterance length, recognized text, and the classifier’s confidence measure reduces the classification error rate to 2.5% of the test sets.

2006-ju-wang-acero-icslp.pdf
PDF file

In  the International Conference on Spoken Language Processing

Publisher  International Speech Communication Association
© 2007 ISCA. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the ISCA and/or the author.

Details

TypeInproceedings
Pages2011-2014
AddressPittsburgh, PA, USA
> Publications > Call Analysis with Classification Using Speech and Non-Speech Features