Informatica

In this paper, a new digital watermarking method based on vector quantization (VQ) is proposed. In contrast with conventional VQ‐based watermarking schemes, the mean of sub‐blocks is used to train the VQ codebook. In addition, the Anti‐Gray Coding (AGC) technique is employed to enhance the robustness of the proposed watermarking scheme. In this scheme, the secret keys are used to hide the associated information between the original image and the watermark. Then the set of secret keys will be registered to a trusted third party for future verification. Thus, the original image remains unchanged after the watermark is melted into the set of secret keys. Experimental results show that the watermark can survive various possible attacks. Besides that, the size of the secret keys can be reduced.

The isolated word speech recognition system based on dynamic time warping (DTW) has been developed. Speaker adaptation is performed using speaker recognition techniques. Vector quantization is used to create reference templates for speaker recognition. Linear predictive coding (LPC) parameters are used as features for recognition. Performance is evaluated using 12 words of Lithuanian language pronounced ten times by ten speakers.

An analytical review of recent publications in the area of digital speech signal processing is presented. The aim of the given paper is the analysis of these publications, where Artificial Neural Networks (ANNs) were successfully employed. Numerous methods of ANNs employment are discussed due to identify when and why they are reliable alternative to the conventional adaptive signal processing techniques.

The use of vector quantization for speaker identification is investigated. This method differs from the known methods in that the number of centroids is not doubled but increases by 1 at every step. This enables us to obtain identification results at any number of centroids. This method is compared experimentally with the method (Lipeika and Lipeikienė, 1993a, 1993b), where feature vectors of investigative and comparative speakers are compared directly.

Speaker identification problem is investigated. The identification is carried out comparing feature vectors (parameters of LPC model) of the “criminal” and “suspicious” speakers. Both likelihood ratio and cepstral distances are used for comparing feature vectors. The feature vectors are extracted from pseudostationary parts of speech utterances. The identification approach is suitable for text-dependent and text-independent identification. Experimental results illustrate the performance of the algorithm.