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 …
Read moreThe 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.
