Continuous Hindi Speech Recognition Model Based on Kaldi ASR Toolkit

被引：0
|
作者：
Upadhyaya, Prashant [1 ]
Farooq, Omar [1 ]
Abidi, Musiur Raza [1 ]
Varshney, Yash Vardhan [1 ]
机构：
[1] Aligarh Muslim Univ, Dept Elect Engn, Aligarh 202002, Uttar Pradesh, India
来源：
2017 2ND IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET) | 2017年
关键词：
Kaldi ASR; Weight Finite State Transducers; MFCC; Speech recognition;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, continuous Hindi speech recognition model using Kaldi toolkit is presented. For recognition, MFCC and PLP features are extracted from 1000 phonetically balanced Hindi sentence from AMUAV corpus. Acoustic modeling was performed using GMM-HMM and decoding is performed on so called HCLG which is construted from Weight Finite State Transducers (WFSTs). Performance of both monophone and triphone model using N-gram language model is reported which is computed in term of word error rate (WER). A significant reduction in word error rate (WER) was observed using the triphone model. Further, it was found that MFCC feature provide higher recognition accuracy than PLP feature. Goal is to show the performance of Hindi language using present state-of-the-art (Kaldi) system.
引用
收藏
页码：786 / 789
页数：4
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