By Topic

Noisy speech recognition failure diagnosis using Minimum Message Length decision trees

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Cernak, M. ; Inst. of Inf., Slovak Acad. of Sci., Bratislava ; Darjaa, S.

Current ASR technology lacks of effective failure diagnosis of ASR systems. Figures of merits such as WER are very useful, but donpsilat bring much insight into error patterns, error predictions or error analysis of ASR output. This paper explores an application of minimum message length (MML) style decision trees for such a diagnosis, focusing on theoretical background and the failure diagnosis of noisy speech recognition. In addition, the paper focuses on failure diagnosis of noisy speech, covering several kinds of intrinsic speech variabilities as well. Results on added speech-shaped noise at different SNR, ranging from 25 dB to -10 dB, are presented.

Published in:

Systems, Signals and Image Processing, 2008. IWSSIP 2008. 15th International Conference on

Date of Conference:

25-28 June 2008