Discrimination algorithm using voiced detection method and time-delay neural network system by 3 FFT sub-bands Online publication date: Mon, 13-Apr-2015
by Jae Seung Choi
International Journal of Computational Vision and Robotics (IJCVR), Vol. 5, No. 2, 2015
Abstract: In the field of speech processing, the main application of a neural network is the category discrimination of phoneme recognition and speech/speaker recognition. In a speech signal, information on the time variation is significant when training the neural network for the speech signal input. Therefore, this paper proposes a speech discrimination algorithm in noisy speech signals using a voiced detection method and a time-delay neural network with a time structure. Thus, this algorithm first detects voiced sections using the proposed neural network at each frame in the condition of background noises, then discriminates the speech signals using the time-delay neural network based on three fast Fourier transform sub-bands, in the noisy environments. The effectiveness of the proposed algorithm is experimentally confirmed based on measuring the correct discrimination rates for speech degraded by various noises.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook