Volume 23, Issue 2 (1-2005)                   2005, 23(2): 1-10 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

A. Sayadiyan, K. Badi, M. Moin and N. Moghadam. Presentation of K Nearest Neighbor Gaussian Interpolation and comparing it with Fuzzy Interpolation in Speech Recognition. Journal of Advanced Materials in Engineering (Esteghlal) 2005; 23 (2) :1-10
URL: http://jame.iut.ac.ir/article-1-314-en.html
Abstract:   (6306 Views)
Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very high, especially in very large discrete utterance recognition problems. For real time implementation of very large discrete utterance recognition, we must use discrete density HMM (DDHMM). To increase the performance of DDHMM, one usual solution is fuzzy interpolation. In this study, we present a new method named Gaussian interpolation. We implemented and compared the performance of two types of interpolation methods for 1500 Persian speech command words. Results show that precision and flexibility of Gaussian interpolation is better thanthose of the fuzzy interpolation.
Full-Text [PDF 138 kb]   (1545 Downloads)    
Type of Study: Research | Subject: General
Received: 2014/10/25 | Published: 2005/01/15

Add your comments about this article : Your username or Email:
CAPTCHA

© 2024 CC BY-NC 4.0 | Journal of Advanced Materials in Engineering (Esteghlal)

Designed & Developed by : Yektaweb