Speaker Recognition for Hindi Speech Signal using MFCC-GMM Approach

被引：32
|
作者：
Maurya, Ankur [1 ]
Kumar, Divya [1 ]
Agarwal, R. K. [2 ]
机构：
[1] Motilal Nehru Natl Inst Technol Allahabad, Allahabad 211004, Uttar Pradesh, India
[2] Natl Inst Technol Kurukshetra, Kurukshetra 136119, Haryana, India
来源：
6TH INTERNATIONAL CONFERENCE ON SMART COMPUTING AND COMMUNICATIONS | 2018年 / 125卷
关键词：
Identification rate (IR); MFCC-GMM; MFCC-VQ; VERIFICATION; IDENTIFICATION;
D O I：
10.1016/j.procs.2017.12.112
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Speaker recognition for different languages is still a big challenge for researchers. The accuracy of identification rate (IR) is great issue, if the utterance of speech sample is less. This paper aims to implement speaker recognition for Hindi speech samples using Mel frequency cepestral coffiecient vector quantization (MFCC-VQ) and Mel frequency cepestral cofficient-Gaussian mixture model (MFCC-GMM) for text dependent and text independent phrases. The accuracy of text independent recognition by MFCC-VQ and MFCC-GMM for Hindi speech sample is 77.64% and 86.27% respectively. However, the accuracy has increased significantly for text dependent recognition. The accuracy of Hindi speech samples are 85.49 % and 94.12 % using MFCC-VQ and MFCC-GMM approach. We have tested 15 speakers consisting 10 male and 5 female speakers. The total number of trails for each speaker is 17. (C) 2018 The Authors. Published by Elsevier B.V.
引用
收藏
页码：880 / 887
页数：8
相关论文
共 50 条
  • [41] Analysis of Throat Microphone Using MFCC Features for Speaker Recognition
    Visalakshi, R.
    Dhanalakshmi, P.
    Palanivel, S.
    COMPUTATIONAL INTELLIGENCE, CYBER SECURITY AND COMPUTATIONAL MODELS, ICC3 2015, 2016, 412 : 35 - 41
  • [42] GMM-UBM Modeling for Speaker Recognition on a Romanian Large Speech Corpora
    Georgescu, Alexandru-Lucian
    Cucu, Horia
    2018 12TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS (COMM), 2018, : 547 - 551
  • [43] Speaker Identification with Whispered Speech mode Using MFCC: Challenges to Whispered Speech Identification
    Sardar, V. M.
    Shrbahadurkar, S. D.
    2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING (ICIP), 2015, : 70 - 74
  • [44] Enhancement in speaker recognition for optimized speech features using GMM, SVM and 1-D CNN
    Sumita Nainan
    Vaishali Kulkarni
    International Journal of Speech Technology, 2021, 24 : 809 - 822
  • [45] Vocal Fold Disorder Detection based on Continuous Speech by using MFCC and GMM
    Ali, Zulfiqar
    Alsulaiman, Mansour
    Muhammad, Ghulam
    Elamvazuthi, Irraivan
    Mesallam, Tamer A.
    2013 7TH IEEE GCC CONFERENCE AND EXHIBITION (GCC), 2013, : 292 - 297
  • [46] ON COMBINING DNN AND GMM WITH UNSUPERVISED SPEAKER ADAPTATION FOR ROBUST AUTOMATIC SPEECH RECOGNITION
    Liu, Shilin
    Sim, Khe Chai
    2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2014,
  • [47] Enhancement in speaker recognition for optimized speech features using GMM, SVM and 1-D CNN
    Nainan, Sumita
    Kulkarni, Vaishali
    INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY, 2021, 24 (04) : 809 - 822
  • [48] Speech Based Human Emotion Recognition Using MFCC
    Likitha, M. S.
    Gupta, Raksha R.
    Hasitha, K.
    Raju, A. Upendra
    2017 2ND IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET), 2017, : 2257 - 2260
  • [49] Emotion Recognition in Speech Using MFCC and Wavelet Features
    Kishore, K. V. Krishna
    Satish, P. Krishna
    PROCEEDINGS OF THE 2013 3RD IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC), 2013, : 842 - 847
  • [50] Speech Emotion Recognition Using ANN on MFCC Features
    Dolka, Harshit
    Xavier, Arul V. M.
    Juliet, Sujitha
    ICSPC'21: 2021 3RD INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION (ICPSC), 2021, : 431 - 435
12345