Abstract
Robust feature extraction techniques play an important role in speaker recognition system. Four speech feature extraction techniques such as Mel-Frequency Cepstral Coefficient (MFCC), Linear Prediction Cepstrum Coefficient (LPCC), Perceptual Linear Predictive (PLP), and Wavelet Cepstral Coefficient (WCC) techniques are analyzed for extracting speaker-specific information. The design of WCC method is done for this work. Hidden Markov Model (HMM) is used to model each speaker from the speaker-specific speech features. The conventional Person Identification System (PIS) is normally employed in an environment where the background noise is unavoidable. To simulate such environment, an additive white Gaussian noise of different SNRs is added with a studio quality speech data. Evaluation of PIS is performed using the Hidden Markov Toolkit (HTK). Multiple experiments are performed. Acoustic modeling of speaker and evaluation is done for clean and noisy environment. The experiment results indicate that 100% accuracy for text-independent PIS in a clean environment. Furthermore, it is observed that MFCC is proven to be better noise robust than PLP and LPC. It is also noted that dynamic features such as delta and acceleration features are combined with static features improve the performance of the PIS in noisy environment.
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References
Kartik R, Prasanna P, Vara Prasad S (2008) Multimodal biometric person authentication system using speech and signature features. In: TENCON 2008—2008 IEEE region 10 conference 2008, pp 1–6; Maxwell JC (1892) A treatise on electricity and magnetism, 3rd edn. vol 2, Oxford: Clarendon, pp 68–73
Martinez J, Perez H, Escamilla E, Suzuki (2012) Speaker recognition using Mel frequency cepstral coefficients (MFCC) and vector quantization (VQ) techniques. In: 22nd international conference on electrical communication and computers (CONIELECOMP), 2012 pp 248–251.K
Malode SL, Sahare AA (2012) An improved speaker recognition by using VQ & HMM. In: 3rd international conference on sustain energy international system (SEISCON 2012), IET Chennai pp 1–7; Yorozu Y, Hirano M, Oka K, Tagawa Y (1987) Electron spectroscopy studies on magneto-optical media and plastic substrate interface. IEEE Transl J Magn Japan 2:740–741 (Digests 9th Annu)
Tripathi S, Bhatnagar S (2012) Speaker recognition. In: 2012 third international conference on computer and communication technology (ICCCT), pp 283–287
Tiwari V (2010) MFCC and its applications in speaker recognition. Int J Emerg Technol 1(1):19–22
Dave N (2013) Feature extraction methods LPC, PLP and MFCC in speech recognition. Int J Adv Res Eng Technol 1(VI)
Mukhedhkar AS, Alex JSR (2014) Robust feature extraction methods for speech recognition in noisy environments. In: 2014 first international conference on networks & soft computing 978-1-4799-3486-7/14/$31.00 _c 2014 IEEE
Sumithra MG, Thanuskodi K, Archana AHJ (2011) A new speaker recognition system with combined feature extraction techniques, vol 7, issue 4. Department of electronics and communication engineering, pp 459–465
Dhonde SB, Jagade SM Feature extraction techniques in speaker recognition: a review. Int J Recent Technol Mech Electr Eng (IJRMEE) 2(5):104–106 ISSN: 2349-7947
Chaudhari PR, Alex JSR (2016) Selection of features for emotion recognition from speech. Indian J Sci Technol 9(39). https://doi.org/10.17485/ijst/2016/v9i39/95585
Study of speaker recognition systems. [Online]. Available: http://ethesis.nitrkl.ac.in/2450/1/Project_Report.pdf. Accessed 17 Feb 2015
www.ee.columbia.edu/ln/LabROSA/doc/HTKBook21/node3.htmlorr, speech.ee.ntu.edu.tw/homework/DSP_HW2-1/htkbook.pdf
Rabiner LR (1989) A tutorial on hidden Markov models and selected application in speech recognition. Proc IEEE 77(2)
Allabakash S et al (2015) Wavelet transform-based methods for removal of ground clutter and denoising the radar wind profiler data. 9:440–448.
Cho J, Park H (2016) Independent vector analysis followed by HMM-based feature enhancement for robust speech recognition. Sig Process 120:200–208. Available at: http://dx.doi.org/10.1016/j.sigpro.2015.09.002
Daubechies I (1997) Ten lectures on wavelets. SIAM, Philadelphia, USA
Starang G, Nguyen T (1997) Wavelets and filter banks. Wellesley-Cambridge press, Wellesley MA, USA
Mallat S (1998) A wavelet tour of signal processing. Academic, New York, 1998
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Chaudhari, P.R., Alex, J.S.R. (2018). Evaluation of Cepstral Features of Speech for Person Identification System Under Noisy Environment. In: Nandi, A., Sujatha, N., Menaka, R., Alex, J. (eds) Computational Signal Processing and Analysis. Lecture Notes in Electrical Engineering, vol 490. Springer, Singapore. https://doi.org/10.1007/978-981-10-8354-9_17
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DOI: https://doi.org/10.1007/978-981-10-8354-9_17
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