Categorization of Hindi phonemes by neural networks.

Autor:	A. Dev, S. S. Agrawal, D. R. Choudhury
Předmět:	PHONEMICS INDIGENOUS languages of the Americas HINDI language ARTIFICIAL neural networks
Zdroj:	AI & Society; Nov2003, Vol. 17 Issue 3/4, p375-382, 8p
Abstrakt:	The prime objective of this paper is to conduct phoneme categorization experiments for Indian languages. In this direction a major effort has been made to categorize Hindi phonemes using a time delay neural network (TDNN), and compare the recognition scores with other languages. A total of six neural nets aimed at the major coarse of phonetic classes in Hindi were trained. Evaluation of each net on 350 training tokens and 40 test tokens revealed a 99% recognition rate for vowel classes, 87% for unvoiced stops, 82% for voiced stops, 94.7% for semi vowels, 98.1% for nasals and 96.4% for fricatives. A new feature vector normalisation technique has been proposed to improve the recognition scores. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink