Voice Clustering Gender Using Fuzzy Possibilistic C-Means Standard

To recognize a voice pattern the computer requires a standard and logical mechanism. The main problem is how process acquisition data by generating a number of numerical data are representative and consistent.Voice recognition system use feature extraction techniques based on domain time with the two methods are short-time energy and zero crossing rate. Steps being taken is prepared ten audios, process feature extraction method based on domaintime, using a Short Time Energy, Zero Crossing Rate, and clustering using Fuzzy Possibilistic C-Means Standard. Step method voice recognition are input transducer for analyzing input of electronic signal, prepossessing to add signal condition including signal amplification, spectrum analysis and digital conversion, feature extraction to comparing template matching, response selector for selecting input pattern in software using the technique of searching, sorting, least squares analysis, and output system to show result application process.

[1]  O. Lartillot,et al.  A MATLAB TOOLBOX FOR MUSICAL FEATURE EXTRACTION FROM AUDIO , 2007 .

[2]  John H. L. Hansen,et al.  Unsupervised audio stream segmentation and clustering via the Bayesian information criterion , 2000, INTERSPEECH.

[3]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[4]  Ponani S. Gopalakrishnan,et al.  Clustering via the Bayesian information criterion with applications in speech recognition , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[5]  M. A. Siegler,et al.  Automatic Segmentation, Classification and Clustering of Broadcast News Audio , 1997 .

[6]  S. Furui,et al.  Unsupervised speaker adaptation method based on hierarchical spectral clustering , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[7]  Itshak Lapidot,et al.  Unsupervised speaker recognition based on competition between self-organizing maps , 2002, IEEE Trans. Neural Networks.

[8]  Xiao-Hong Wu,et al.  A Possibilistic C-Means Clustering Algorithm Based on Kernel Methods , 2006, 2006 International Conference on Communications, Circuits and Systems.

[9]  F. Kubala,et al.  Automatic Speaker Clustering , 1997 .

[10]  L. Hubert,et al.  Comparing partitions , 1985 .

[11]  Jean-Claude Junqua,et al.  Towards domain independent speaker clustering , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[12]  Tetsuo Kosaka,et al.  Tree-structured speaker clustering for fast speaker adaptation , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  Douglas A. Reynolds,et al.  Blind clustering of speech utterances based on speaker and language characteristics , 1998, ICSLP.

[14]  Sue E. Johnson,et al.  Who spoke when? - automatic segmentation and clustering for determining speaker turns , 1999, EUROSPEECH.

[15]  Herbert Gish,et al.  Clustering speakers by their voices , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[16]  J. Bezdek,et al.  Recent convergence results for the fuzzy c-means clustering algorithms , 1988 .