A support vector machines-based rejection technique for speechrecognition
Changxue Ma
Randolph, M.A.
Drish, J.
Human Interface Lab., Motorola Labs., Schaumburg, IL;
This paper appears in: Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Publication Date: 2001
Volume: 1,
On page(s): 381-384 vol.1
Meeting Date: 05/07/2001 - 05/11/2001
Location: Salt Lake City, UT, USA
ISBN: 0-7803-7041-4
References Cited: 6
INSPEC Accession Number: 7120357
DOI: 10.1109/ICASSP.2001.940847
Posted online: 2002-08-07 00:29:42.0
Abstract
Support vector machines represent a new approach to pattern
classification developed from the theory of structural risk
minimization. In this paper, we present an investigation into the
application of support vector machines to the confidence measurement
problem in speech recognition. Specifically, based on the results from
an initial decoding of an utterance during speech recognition, we derive
a feature vector consisting of parameters such as word score density,
N-best word score density differences, relative word score and relative
word duration as input to the confidence measurement process in which
hypothetically correct utterances are accepted and utterances determined
to be incorrect are rejected. We propose a new approach to training
support vector machines. In this paper, we train and test a support
vector machines classifier and compare the results with other
statistical classification methods
Index Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
