By Topic

Joint feature and model training for minimum detection errors applied to speech subword detection

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
$33 $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)
Johnsen, M.H. ; Dept. of Electron. & Telecommun., NTNU, Trondheim, Norway ; Canterla, A.M.

This paper presents methods and results for joint optimization of the feature extraction and the model parameters of a detector. We further define a discriminative training criterion called Minimum Detection Error (MDE). The criterion can optimize the F-score or any other detection performance metric. The methods are used to design detectors of subwords in continuous speech, i.e. to spot phones and articulatory features. For each subword detector the MFCC filterbank matrix and the Gaussian means in the HMM models are jointly optimized. For experiments on TIMIT, the optimized detectors clearly outperform the baseline detectors and also our previous MCE based detectors. The results indicate that the same performance metric should be used for training and test and that accuracy outperforms F-score with respect to relative improvement. Furter, the optimized filterbanks usually reflect typical acoustic properties of the corresponding detection classes.

Published in:

Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on

Date of Conference:

23-26 Sept. 2012