This paper presents a new blind digital speech watermarking technique based on Eigenvalue quantization in Discrete Wavelet Transform. Initially, each frame of the digital speech was transformed into the wavelet domain by applying Discrete Wavelet Transform. Then, the Eigenvalue of Approximation Coefficients was computed by using Singular Value Decomposition. Finally, the watermark bits were embedded by quantization of the Eigen-value. The experimental results show that this watermarking technique is robust against different attacks such as filtering, additive noise, resampling, and cropping. Applying new robust transforms, adaptive quantization steps and synchronization techniques can be the future trends in this field. ª 2014 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Digital speech watermarking is a robust way to hide and thus secure data like audio and video from any intentional or unintentional manipulation through transmission. In terms of some signal characteristics including bandwidth, voice/non-voice and production model, digital speech signal is different from audio, music and other signals. Although, various review articles on image, audio and video watermarking are available, there are still few review papers on digital speech watermarking. Therefore this article presents an overview of digital speech watermarking including issues of robustness, capacity and imperceptibility. Other issues discussed are types of digital speech watermarking, application, models and masking methods. This article further highlights the related challenges in the real world, research opportunities and future works in this area, yet to be explored fully.

In this paper, a semi-fragile and blind digital speech watermarking technique for online speaker recognition systems based on the discrete wavelet packet transform (DWPT) and quantization index modulation (QIM) has been proposed that enables embedding of the watermark within an angle of the wavelet's sub-bands. To minimize the degradation effects of the watermark, these sub-bands were selected from frequency ranges where little speaker-specific information was available (500-3500 Hz and 6000-7000 Hz). Experimental results on the TIMIT, MIT, and MOBIO speech databases show that the degradation results for speaker verification and identification are 0.39 and 0.97 %, respectively, which are negligible. In addition, the proposed watermark technique can provide the appropriate fragility required for different signal processing operations.