MFCC for Voiced Part Using VAD and GMM Based Gender Recognition

MFCC for Voiced Part Using VAD and GMM Based Gender Recognition

Hema Kumar Pentapati Srinivas Vasamsetti Madhu Tenneti

Department of ECE, SIET, Narasapur, W.G.Dt, Andhra Pradesh, India

Corresponding Author Email:,,
26 December 2017
| |
9 January 2018
| | Citation



For many applications, identifying the gender information of a speaker is important. In this paper, we implemented the system which identifies the speaker and also gender of the speaker by using MFCC and GMM in an uncontrolled environment. In this text independent system, we aim on the classification using GMM for the extracted features using MFCC and also the speech signal is processed with Voice Activity Detector (VAD). In the experiments using locally recorded database, the system without voice activity detector (VAD) does not provide accurate results. So, the main aim of this paper is to develop a text independent speaker identification and also gender identification using MFCC along with VAD and GMM which improves the performance further relatively when compared with the system without VAD. The performance of the proposed system tested for 70 speakers with 100 percent recognition rate is achieved based on the log likelihood scores.


Mel frequency cepstral coefficients (MFCC), Vector quantization, Gaussian mixture model (GMM), Voice Activity Detector (VAD)

1. Introduction
2. Review of Literature
3. Gaussian Mixture Model (GMM)
4. Voice Activity Detector (VAD)
5. Experiments
6. Results
7. Conclusion

[1] Ergün Yücesoy, Vasif V. Nabiyev, Gender Identification of a Speaker Using MFCC and GMM, 2013, ELECO 8th international conference.

[2] Lantian Li, Thomas Fang Zheng, Gender¬dependent Feature Extraction for Speaker Recognition, China SIP 2015.

[3] Huang Ting, Yang Yingchun, Wu Zhaohui, Combining MFCC and Pitch to Enhance the Performance of the Gender Recognition.

[4] M.S. Sinith, AnoopSalim, K GowriSankar., K.V. Sandeep Narayanan, Vishnu Soman, A Novel Method for Text-Independent Speaker Identification Using MFCC and GMM ICALIP 2010.

[5] P. Hema Kumar, V. Srinivas, T. Madhu. Improved Dynamic Speaker Recognition System using NLMS Adaptive Filter, 2016, International Journal of Computer Applications, vol. 148, no. 10. 

[6] R. Bachu, B.K. Adapa, S. Kopparthi. Barkana Buket, Separation of Voiced and Unvoiced Speech Signals using Energy and Zero Crossing Rate, 2008.

[7] Wenyong Lin, An improved GMM based clustering algorithm for efficient speaker identification, 2015, 4th International Conference on Computer Science and Network Technology (ICCSNT 2015).

[8] Michael Lutter, Mel Frequency Cepstral Coefficients (feature extraction/MFCC). The Speech Recognition Wiki25 November 2014.

[9] J.P. Campbell, Speaker Recognition: A Tutorial, 1997, Proc. Of the IEEE, vol. 85, no. 9, pp. 1437-1462.

[10] Vibha Tiwari, MFCC and its applications in speaker recognition, 2010, International Journal on Emerging Technologies, vol. 1, no. 1, pp. 19-22.

[11] Rania Chakroun, Leila Beltaïfa Zouari, Mondher Frikha, Ahmed Ben Hamida, Improving Text-independent speaker recognition with GMM, 2nd International Conference on Advanced Technologies for Signal and Image Processing - ATSIP'2016 March 21-24, 2016, Monastir, Tunisia.

[12] Vaishali M. Karne, Akhilesh Singh Thakur, Vibha Tiwari, Least Mean Square (LMS) Adaptive Filter For Noise Cancellation, International Journal of Application or Innovation in Engineering & Management (IJAIEM), ISSN 2319–4847.

[13] Sourjya Sarkar, K. Sreenivasa Rao, Speaker Verification in Noisy Environment Using GMM Supervectors, 2013.

[14] Sheng Zhang, Jiashu Zhang, Hongyu Han, Robust Variable Step-Size Decorrelation Normalized Least-Mean Square Algorithm and its Application to Acoustic Echo Cancellation IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[15] Xin-xing ling, Ling Zhan, Hong Zhao, Ping Zhou Speaker Recognition System Using the Improved GMM-based Clustering Algorithm.

[16] Jayant M. NaikSpeaker Verification: A Tutorial January 1990 - IEEE Communications Magazine.

[17] Yuan Liu, Tianfan Fu, Yuchen Fan, YanminQian, Kai Yu Speaker Verification with Deep Features2014 International Joint Conference on Neural Networks (IJCNN), July 6-11, 2014, Beijing, China.

[18] SourjyaSarkar, K. Sreenivasa Rao, Significance of Utterance Partitioning in GMM-SVM Based Speaker Verification in Varying Background Environment, A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition Md Sahidullah, Student Member, IEEE, Goutam Saha, Member, IEEE.

[19] RandheerBagi, JainathYadav, K. Sreenivasa Rao, Improved Recognition Rate of Language Identification System in Noisy Environment.

[20] Douglas A. Reynolds Speaker Identification and verification using Gaussian Mixture speaker models speech communication, 1995, vol. 17, ELSEVIER.