Speaker identification analysis for SGMM with k -means and fuzzy C -means clustering using SVM statistical technique

Abstract

Speaker Identification denotes the speech samples of known speaker and it identifies the best matches of the input model. The SGMFC method is the combination of Sub Gaussian Mixture Model (SGMM) with the Mel-frequency Cepstral Coefficients (MFCC) for feature extraction. The SGMFC method minimizes the error rate, memory footprint and also computational throughput measure needs of a medium-vocabulary speaker identification system, supposed for preparation on a transportable or otherwise. Fuzzy $C$ -means and $k$ -means clustering are used in the SGMM method to attain the improved efficiency and their outcomes with parameters such as precision, sensitivity and specificity are compared.

Keywords

k-means fuzzy C-means SGMFC speaker identification SVM

Get full access to this article

View all access options for this article.

References

Reynolds

and Rose

, Robust text-independent speaker identification using Gaussian mixture speaker models, IEEE Trans Speech Audio Process 3(1) (1995), 72–83.

Makhoul

, Linear prediction: A tutorial review, Proc IEEE 63(4) (1975), 561–580.

Reynolds

, Large population speaker identification using clean and telephone speech, IEEE Signal Process Lett 2(3) (1995), 46–48.

Reynolds

, Speaker identification and verification using Gaussian mixture speaker models, Speech Commun 17(1–2) (1995), 91–108.

Pellom

and Hansen

, An efficient scoring algorithm for gaussian mixture model based speaker identification, IEEE Signal Process Lett 5(11) (1998), 281–284.

Baraldi

and Blonda

, A survey of fuzzy clustering algorithms for pattern identification, IEEE Trans Syst, Man, Cybern, B: Cybern 29(6) (1999), 778–785.

Reynolds

Quatieri

and Dunn

, Speaker verification using adapted Gaussian mixture models, Digital Signal Process 10(1–3) (2000), 19–41.

Wang

, Prosodic modeling for improved speech identification and understanding, Ph.D. dissertation, Mass Inst of Technol, Cambridge, MA, 2001.

Ezzaidi

Rouat

and O’Shaughnessy

, Towards combining pitch and mfcc for speaker identification systems, in: Proc 7th Eur Conf Speech Commun Technol, 2001.

10.

Chaudhari

Navrratil

Ramaswamy

and Maes

, Very large population text-independent speaker identification using transformation enhanced multi-grained models, in: Proc IEEE Int Conf Acoust, Speech, Signal Process (ICASSP’01) 1 (2001), 461–464.

11.

De Cheveigné

and Kawahara

, Yin, a fundamental frequency estimator for speech and music, J Acoust Soc Amer 111 (2002), 1917.

12.

Hosseinzadeh

and Krishnan

, Combining vocal source and MFCC features for enhanced speaker identification performance using GMMS, in: IEEE 9th Workshop Multimedia Signal Process MMSP’07, 2007, pp. 365–368.

13.

Grimaldi

and Cummins

, Speaker identification using instantaneous frequencies, IEEE Trans Audio, Speech, Lang Process 16(6) (2008), 1097–1111.

14.

Apsingekar

and De Leon

, Speaker model clustering for efficient speaker identification in large population applications, IEEE Trans Audio, Speech, Language Process 17(4) (2009), 848–853.

15.

Sarkar

Rath

and Umesh

, Fast approach to speaker identification for large population using mllr and sufficient statistics, in: Proc National Conf Commun (NCC), 2010, pp. 1–5.

16.

Togneri

and Pullella

, An overview of speaker identification: Accuracy and robustness issues, IEEE Circuits Syst Mag 11(2) (2011), 23–61.

17.

Dehak

Kenny

Dehak

Dumouchel

and Ouellet

, Front-end factor analysis for speaker verification, IEEE Trans Audio, Speech, Lang Process 19(4) (2011), 788–798.

18.

and Nucci

, Pitch-based gender identification with two-stage classification, Security Commun Netw (2011).

19.

Wang

Ching

Zheng

and Lee

, Robust speaker identification using denoised vocal source and vocal tract features, IEEE Trans Audio, Speech, Lang Process 19(1) (2011), 196–205.

20.

Diez

Penagarikano

Varona

Rodriguez-Fuentes

and Bordel

, On the use of dot scoring for speaker diarization, Pattern Recogn and Image Anal (2011), 612–619.

21.

Nakagawa

Wang

and Ohtsuka

, Speaker characterization and identification-speaker identification and verification by combining mfcc and phase information, IEEE Trans Audio, Speech, Lang Process 20(4) (2012), 1085.

22.

and Nucci

, Fuzzy-clustering-based decision tree approach for large population speaker identification, IEEE Transactions On Audio, Speech, and Language Processing 21(4) (2013).

23.

Chandra

Manikandan

and Sivasankar

, A proportional study on feature extraction method in automatic speech identification system, Ijireeice 2(1) (2014).

24.

Mashao

, A hybrid GMM-SVM speaker identification system, AFRICON, IEEE 1 (2004), 319–322.

25.

Madikeri

Motlicek

and Bourand

, Combining SGMM speaker vectors and KL-HMM approach for Speaker Diarization, ICASSP, IEEE, (2015).

26.

Leon

V.R.A.

, Speaker model clustering for efficient speaker identification in large population applications, IEEE Transactions on Audio, Speech, and Language Processing 17(4) (2009), 848–853.

27.

Povey

and Burget

, The subspace gaussian mixturemodel – a structured model for speech recognition, ComputerSpeech and Language 25(2) (2011), 404–439.

28.

Verma

V.K.

and Khanna

, Indian language identification using k-means clustering and support vector machine (SVM), Engineering and Systems (SCES), IEEE, (2013).

29.

Ghosh

and Kumar Dubery

, Comparative analysis of k-means and fuzzy C-means algorithm, IJACSA 4(4) (2013).

30.

Sahu

and Dharmale

, Controlling the application via speech processing through mel frequency cepstral coefficients and back propagation neural method 3(2) (2016).

31.

Baid

and Talbar

, Comparative mixture, fuzzy C-means algorithms for brain tumor segmentation, ICCASP 137 (2016), 592–597.