Normalization techniques for hiding speakers identity

In most security domain transmission of secret and confidential voice data requires that the identity of the speaker should not get revealed but at the same time data contents in speech signal should be interpreted. Speech and speaker normalization techniques help to solve this issue. This paper proposes speaker normalization technique that attempts to alter the characteristics of speaker's voice so that it would be difficult for an eavesdropper to identify the speaker. Intended listener can retrieve the particular information. Evaluations of subjective and objective tests confirm the effectiveness of the proposed method and because of this method security enhances.

[2]  Antonio J. Rubio,et al.  Feature extraction combining spectral noise reduction and cepstral histogram equalization for robust ASR , 2002, INTERSPEECH.

[3]  Hermann Ney,et al.  Histogram based normalization in the acoustic feature space , 2001, IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01..

[4]  Ming Liu,et al.  Frequency domain correspondence for speaker normalization , 2007, INTERSPEECH.

[5]  Keikichi Hirose,et al.  Adaptive thresholding approach for robust voiced/unvoiced classification , 2011, 2011 IEEE International Symposium of Circuits and Systems (ISCAS).

[6]  C. Lopes,et al.  VTLN Through Frequency Warping Based on Pitch , 2003 .

[7]  Urmila Shrawankar,et al.  Voice Activity Detector and Noise Trackers for Speech Recognition System in Noisy Environment , 2010, Int. J. Adv. Comp. Techn..

[8]  Md. Jahangir Alam,et al.  COMPARATIVE STUDY OF A PRIORI SIGNAL-TONOISE RATIO (SNR) ESTIMATION APPROACHES FOR SPEECH ENHANCEMENT , 2009 .

[9]  Thomas Fang Zheng,et al.  Pitch Mean Based Frequency Warping , 2006, ISCSLP.

[10]  Philip C. Woodland,et al.  An investigation into vocal tract length normalisation , 1999, EUROSPEECH.

[11]  Thilo Pfau,et al.  A combination of speaker normalization and speech rate normalization for automatic speech recognition , 2000, INTERSPEECH.

[12]  Urmila Shrawankar,et al.  Noise Estimation and Noise Removal Techniques for Speech Recognition in Adverse Environment , 2010, Intelligent Information Processing.

[14]  Simon King,et al.  IEEE Workshop on automatic speech recognition and understanding , 2009 .

[15]  A. Acero,et al.  IMPROVEMENTS ON SPEECH RECOGNITON FOR FAST TALKERS , 1999 .

[16]  Mei-Yuh Hwang,et al.  Improvements on speech recognition for fast talkers , 1999, EUROSPEECH.

[17]  Haizhou Li,et al.  Normalization of the Speech Modulation Spectra for Robust Speech Recognition , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[18]  Urmila Shrawankar,et al.  Adverse Conditions and ASR Techniques for Robust Speech User Interface , 2013, ArXiv.

[19]  Dragisa Miskovic,et al.  Energy Normalization in Automatic Speech Recognition , 2008, TSD.

[20]  M.G. Bellanger,et al.  Digital processing of speech signals , 1980, Proceedings of the IEEE.