Informatica

A possibility to use the formant features (FF) in the user-dependent isolated word recognition has been investigated. The word recognition was performed using a dynamic time-warping technique. Several methods of the formant feature extraction were compared and a method based on the singular prediction polynomials has been proposed for the recognition of isolated words. Recognition performance of the proposed method was compared to that of the linear prediction coding (LPC) and LPC-derived cepstral features (LPCC). In total, 111 Lithuanian words were used in the recognition experiment. The recognition performance was evaluated at various noise levels. The experiments have shown that the formant features calculated from the singular prediction polynomials are more reliable than the LPC and LPCC features at all noise levels.

The paper deals with the use of dynamic programming for word endpoint detection in isolated word recognition. Endpoint detection is based on likelihood maximization. Expectation maximization approach is used to deal with the problem of unknown parameters. Speech signal and background noise energy is used as features for making decision. Performance of the proposed approach was evaluated using isolated Lithuanian words speech corpus.

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.

The problem of text-independent speaker recognition based on the use of vocal tract and residue signal LPC parameters is investigated. Pseudostationary segments of voiced sounds are used for feature selection. Parameters of the linear prediction model (LPC) of vocal tract and residue signal or LPC derived cepstral parameters are used as features for speaker recognition. Speaker identification is performed by applying nearest neighbour rule to average distance between speakers. Comparison of distributions of intraindividual and interindividual distortions is used for speaker verification. Speaker recognition performance is investigated. Results of experiments demonstrate speaker recognition performance.

Language engineering encompassing natural language processing and speech processing became very important for a development of every nation in multilingual Europe. After the Council of European Union approved conclucions on linguistic and cultural diversity, tools and systems created for every European language are necessary to overcome language barriers and to use all languages in various spheres of human cooperation. The paper gives an overview and a consideration of language engineering in Lithuania.

The problem of speaker identification is investigated. Basic segments – pseudostationary intervals of voiced sounds are used for identification. The identification is carried out, comparing average distances between an investigative and comparatives. Coefficients of the linear prediction model (LPC) of a vocal tract, cepstral coefficients and LPC coefficients of an excitation signal are used for identification as features. Three speaker identification methods are presented. Experimental investigation of their performance is discussed.

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.

A review of electrocardiographic (ECG) data compression methods is presented. It shows what data compression techniques are available and what the implementation consideration are for each technique.

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.

The dynamic programming method for estimation of many change-points in univariate autoregressive (AR) sequences with known AR parameters between change-points is investigated. A problem how to use this method for long autoregressive sequences is solved and a constructive solution is given. A simulation experiment illustrates the advantages of the solution obtained.