Fuzzy GMM-based Confidence Measure Towards Keywords Spotting Application
Loading...
Date
2007-08-08T20:13:28Z
Authors
Abida, Mohamed Kacem
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
The increasing need for more natural human machine interfaces has generated intensive
research work directed toward designing and implementing natural speech
enabled systems. The Spectrum of speech recognition applications ranges from understanding
simple commands to getting all the information in the speech signal
such as words, meaning and emotional state of the user. Because it is very hard to
constrain a speaker when expressing a voice-based request, speech recognition systems
have to be able to handle (by filtering out) out of vocabulary words in the users
speech utterance, and only extract the necessary information (keywords) related to
the application to deal correctly with the user query. In this thesis, we investigate
an approach that can be deployed in keyword spotting systems. We propose a confidence
measure feedback module that provides confidence values to be compared
against existing Automatic Speech Recognizer word confidences. The feedback
module mainly consists of a soft computing tool-based system using fuzzy Gaussian
mixture models to identify all English phonemes. Testing has been carried out
on the JULIUS system and the preliminary results show that our feedback module
outperforms JULIUS confidence measures for both the correct spotted words and
the falsely mapped ones. The results obtained could be refined even further using
other type of confidence measure and the whole system could be used for a Natural
Language Understanding based module for speech understanding applications.
Description
Keywords
Confidence measure, Phoneme classification