Automatic Recognition of Dialects of Himachal Pradesh Using MFCC &GMM

被引：0

作者：

Dogra, Ayushi ^{[1
]}

Kaul, Amit ^{[1
]}

Sharma, RavinderNath ^{[1
]}

机构：

[1] NIT, Dept Elect Engn Dept, Hamirpur HP NIT, Hamirpur, HP, India

来源：

PROCEEDINGS OF 2019 5TH IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMPUTING AND CONTROL (ISPCC 2K19) | 2019年

关键词：

Himachali Dialect; GMM; MFCC;

D O I：

10.1109/ispcc48220.2019.8988336

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the present globalized society, people from all over the world are coming and working together. The biggest hurdle in this process is the medium of interaction, as all individuals do not speak a common language. A way to circumvent this problem is to develop a machine based system for recognition of different languages and subsequently employing automatic machine translation mechanism for communication between people from diverse language or dialect groups. A MFCC and GMM based approach for recognition of dialects of Himachal Pradesh has been presented in this paper. Around seven dialects spoken in different regions of Himachal Pradesh have been used in this study. The results obtained shows that dialects spoken in adjacent regions have significant overlap and are difficult to distinguish. Accuracy for offline and online tests was respectively around 80% and 70%.

引用

页码：134 / 137

页数：4

共 50 条

[11] MFCC-GMM based accent recognition system for Telugu speech signals
Mannepalli K.
Sastry P.N.
Suman M.
International Journal of Speech Technology, 2016, 19 (1) : 87 - 93
[12] Isolated Automatic Speech Recognition of Quechua Numbers using MFCC, DTW and KNN
Chacca Chuctaya, Hernan Faustino
Montufar Mercado, Rolfy Nixon
Gonzales Gaona, Jeyson Jesus
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2018, 9 (10) : 24 - 29
[13] Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines
Peng, Pingan
He, Zhengxiang
Wang, Liguan
SHOCK AND VIBRATION, 2019, 2019
[14] Audio-Visual Automatic Speech Recognition Using PZM, MFCC and Statistical Analysis
Debnath, Saswati
Roy, Pinki
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE, 2021, 7 (02): : 121 - 133
[15] Automatic Noise Recognition Based on Neural Network Using LPC and MFCC Feature Parameters
Haghmaram, Reza
Aroudi, Ali
Aiagh, Mohammad Hossein Ghezel
Veisi, Hadi
2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2012, : 69 - 73
[16] Automatic Emotion Recognition using Generative and Discriminative Classifiers in the GMM Mean Space
Trabelsi, Imen
Amami, Rimah
Ellouze, Noureddine
2016 2ND INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SIGNAL AND IMAGE PROCESSING (ATSIP), 2016, : 767 - 770
[17] Implementation of Automatic Speaker Recognition on TMS320C6713 Using MFCC
Bhalerao, A. S.
Malode, V. B.
2013 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS, 2013,
[18] AUTOMATIC EMOTION RECOGNITION IN SPEECH SIGNAL USING TEAGER ENERGY OPERATOR AND MFCC FEATURES
He, Ling
Lech, Margaret
Allen, Nicholas
2011 3RD INTERNATIONAL CONFERENCE ON COMPUTER TECHNOLOGY AND DEVELOPMENT (ICCTD 2011), VOL 3, 2012, : 695 - 699
[19] Isolated Word Automatic Speech Recognition (ASR) System using MFCC, DTW & KNN
Imtiaz, Muhammad Atif
Raja, Gulistan
2016 ASIA PACIFIC CONFERENCE ON MULTIMEDIA AND BROADCASTING (APMEDIACAST), 2016, : 106 - 110
[20] Speech Disorder Recognition using MFCC
Jhawar, Gunjan
Nagraj, Prajacta
Mahalakshmi, P.
2016 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), VOL. 1, 2016, : 246 - 250

← 1 2 3 4 5 →