By Topic

Speech/non-speech classification using multiple features for robust endpoint 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
$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

4 Author(s)
Won-Ho Shin ; Inf. Technol. Lab., LG Corp. Inst. of Technol., South Korea ; Byoung-Soo Lee ; Yun-Keun Lee ; Jong-Seok Lee

In this paper, we describe a new speech/non-speech classification method that improves the endpoint detection performance for speech recognition in noisy environments. The proposed method uses multiple features to increase the robustness in noisy environments, and the classification and regression tree (CART) technique is applied to effectively combine these multiple features for classification of each frame. We evaluate the performance of the proposed method by conducting speech/non-speech classification experiments on noisy speech. We also investigate the importance of various features on speech/non-speech classification in noisy environments In particular, the proposed method is applied to the endpoint detection algorithm for isolated speech recognition of a voice-dialing cellular phone. We simulate the speech recognition experiments in various noise environments, and the effects of the proposed method on speech recognition performance are evaluated

Published in:

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:3 )

Date of Conference:

2000