期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2016
卷号：42
期号：2
页码：102-107
DOI：10.14445/22312803/IJCTT-V42P117
出版社：Seventh Sense Research Group
摘要：this paper addresses the problem of large vocabulary speaker independent continuous speech recognition using the phonemes, Hidden Markov Model (HMM) and Normal fit method. Here we first detect for the voiced part in speech signal through computing dynamic threshold in each frame. Real Cepstrum coefficients are extracted as features from the voiced frames. The Baum–Welch algorithm is applied for training those features. Then normal fit technique is applied, the outputted values are labelled using correspondent phoneme or syllable. The model is tested for 5 languages namely English, Kannada, Hindi, Tamil and Telugu. The automatic segmentation of speech signals average accuracy rate is 95.42% and miss rate of about 4.58%. In the large vocabulary, average Word Recognition Rate (WRR) is 85.16% and average Word Error Rate (WER) is 14.84%. All computations are done using mat lab.
