Performance of speaker recognition system using shifted mfcc, delta spectral cepstral coefficient (DSCC) and Fuzzy techniques

  • Authors

    • Priyanka Bansal
    • Syed Akhtar Imam
    2018-03-19
    https://doi.org/10.14419/ijet.v7i2.8.10424
  • Delta Spectrum Cepstral Coefficient (DSCC), Discrete Cosine Transform (DCT), Mel Frequency Cepstral Coefficient (MFCC), Support Vector Classifier(SVC).

  • Speech and speaker recognition systems are biometric inspired systems which are having scope in various online and offline applications. In case of biometric we ponder the variability of speech signal due to the presence of noise which greatly degrades the efficiency of Automatic Speaker Recognition (ASR) in real-world environmental circumstances. Real world speech signal is degraded by different types of noise signals like background noise, interference noise and crosstalk noise. In this paper, we have used Delta Spectrum Cepstrum Coefficient (DSCC) and Shifted MFCC with fuzzy modeling techniques to rectify the deed of ASR even in a noisy surrounding with the help of upgraded speech information which is present at high frequency in the spectral domain. The combination of fuzzy modeling and DSCC creates a firm cumulative algorithm which has reasonably high robustness to noise. Experimental results show that accuracy has enhanced by 10-20% even at 5-8dB SNR in the presence of background noise or turbulent environmental condition or in the presence of white noise.Thus proposed model has improved maturity level in comparison to obsolete methods.

  • References

    1. [1] Anzar S.M. (2016), Efficient online and offline template update mechanisms for speaker recognition, Computers & Electrical Engineering, (50)10-25.

      [2] Bansal P.(2017), A probabilistic Feature Based SVM Model for English Speech Recognition. Journal of Engineering Technology, ( 6) 35 -44.

      [3] Bansal P., Imam S., Bharti Roma(2015), Speaker recognition using MFCC, shifted MFCC with vector quantization and fuzzy. International Conference on Soft Computing Techniques and Implementations,

      [4] Hans D.(2000), The Aurora database for speaker recognition performance. , International Conference on Spoken Language Processing.

      [5] Huang Z.,SabatoSiniscalchi, Chin Hui Lee,(2016), A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition, Neurocomputing, (218), 448-459.

      [6] Kumar Kshitiz (2011), Delta-spectral cepstral coefficients for robust speech recognition. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 4784 – 4787.

      [7] Mukherjee R.(2013), Text dependent speaker recognition using shifted MFCC . Proceedings of IEEE southeastcon, 1 – 4.

      [8] A Fuzzy Nearest Neighbor Classifier for Speaker Identification. IEEE International Conference on Computational Intelligence and Communication Networks, 842 – 845.

      [9] Varela O. (2011), Robust speech detection for noisy environments. IEEE transactions on Aerospace And Electronic Systems Magazine,( 26) 16 – 23.

      [10] S.V.Manikanthan and K.Baskaran “Low Cost VLSI Design Implementation of Sorting Network for ACSFD in Wireless Sensor Networkâ€, CiiT International Journal of Programmable Device Circuits and Systems,Print: ISSN 0974 – 973X & Online: ISSN 0974 – 9624, Issue : November 2011, PDCS112011008.

      [11] T.Padmapriya, Ms. N. Dhivya, Ms U. Udhayamathi, “Minimizing Communication Cost In Wireless Sensor Networks To Avoid Packet Retransmissionâ€, International Innovative Research Journal of Engineering and Technology, Vol. 2, Special Issue, pp. 38-42.

      [12] N.Prathima, K.Hari Kishore, “Design of a Low Power and High Performance Digital Multiplier Using a Novel 8T Adderâ€, International Journal of Engineering Research and Applications, ISSN: 2248-9622, Vol. 3, Issue.1, Jan-Feb., 2013.

  • Downloads

  • How to Cite

    Bansal, P., & Akhtar Imam, S. (2018). Performance of speaker recognition system using shifted mfcc, delta spectral cepstral coefficient (DSCC) and Fuzzy techniques. International Journal of Engineering & Technology, 7(2.8), 278-283. https://doi.org/10.14419/ijet.v7i2.8.10424