Vytautas Arminas

Bio: Vytautas Arminas is an academic researcher from Vilnius Gediminas Technical University. The author has contributed to research in topics: Feature extraction & Field-programmable gate array. The author has an hindex of 3, co-authored 4 publications receiving 37 citations.

Hardware Accelerated FPGA Implementation of Lithuanian Isolated Word Recognition System

Abstract: Enhancement of FPGA implementation of Lithuanian isolated word recognition system is presented. Software based recognizer implementation was used as the basis for enhancement. The feature extraction (as the most time required process) and local distance calculation (as the most times performed process) were selected for hardware implementation. Reduction of recording quality of speech was selected as the way to reduce the amount of the data to analyze. Experimental testing shows correctness of made solutions. Integration of Fast Fourier Transform module reduced the recognition time by 1.6 times, and lower quality of records increased the recognition rate by 2.8 % for speaker dependent and by 4.2 % for speaker independent recognition. The overall achieved acceleration is 6 times, average time of recognition of one word is 15.7 s. Ill. 8, bibl. 14. (in English; summaries in English, Russian and Lithuanian).

Ant System Implementation using Microblaze: Some Preliminary Results on Efficiency Study

Abstract: The paper presents some preliminary results on efficiency study of Ant System implementation using software processor Microblaze. By the use of Monte-Carlo tests of number π calculation the best use of Pseudo-Random Number Generator – implementation of Multiply-With-Carry algorithm in a single precision floating point numbers – is grounded. By experimentation the usefulness of eight supplemental Microblaze core units is assessed and the advantage of the use of Basic Floating Point Unit together with 32 bits Integer Multiplier is proven. Experimental investigation of Traveling Salesman Problem solution by implemented Ant System is presented and confirms that rapid growth of standard deviation may be used as an indicator that system should be adjusted for current complexity of the problem. Ill. 2, bibl. 13. (in English; summaries in English, Russian and Lithuanian).

Acceleration of feature extraction for FPGA-based speech recognition

Abstract: The paper describes a field programmable gate array implementation of the main part of speech recognition system - feature extraction. In order to accelerate recognition the whole cepstral analysis scheme is implemented in hardware by the use of intellectual property cores. Two field programmable gate array devices are used for evaluation. Comparative experimental results of four different implementations are presented. They grounds achieved 29 times faster speech analysis in comparison with software based analysis subsystem.

Hardware Accelerated FPGA Implementation of Lithuanian Isolated Word Recognition System

Abstract: Enhancement of FPGA implementation of Lithuanian isolated word recognition system is presented. Software based recognizer implementation was used as the basis for enhancement. The feature extraction (as the most time required process) and local distance calculation (as the most times performed process) were selected for hardware implementation. Reduction of recording quality of speech was selected as the way to reduce the amount of the data to analyze. Experimental testing shows correctness of made solutions. Integration of Fast Fourier Transform module reduced the recognition time by 1.6 times, and lower quality of records increased the recognition rate by 2.8 % for speaker dependent and by 4.2 % for speaker independent recognition. The overall achieved acceleration is 6 times, average time of recognition of one word is 15.7 s. Ill. 8, bibl. 14. (in English; summaries in English, Russian and Lithuanian).

Artificial neural network-based speech recognition using dwt analysis applied on isolated words from oriental languages

Abstract: Speech recognition is an emerging research area having its focus on human computer interactions (HCI) and expert systems. Analyzing speech signals are often tricky for processing, due to the non-stationary nature of audio signals. The work in this paper presents a system for speaker independent speech recognition, which is tested on isolated words from three oriental languages, i.e., Urdu,Persian, and Pashto. The proposed approach combines discrete wavelet transform (DWT) and feed-forward artificial neural network (FFANN) for the purpose of speech recognition. DWT is used for feature extraction and the FFANN is utilized for the classification purpose. The task of isolated word recognition is accomplished with speech signal capturing, creating a code bank of speech samples, and then by applying pre-processing techniques.For classifying a wave sample, four layered FFANN model is used with resilient back-propagation (Rprop). The proposed system yields high accuracy for two and five classes.For db-8 level-5 DWT filter 98.40%, 95.73%, and 95.20% accuracy rate is achieved with 10, 15, and 20 classes, respectively. Haar level-5 DWT filter shows 97.20%, 94.40%, and 91% accuracy ratefor 10, 15, and 20 classes, respectively. The proposed system is also compared with a baseline method where it shows better performance. The proposed system can be utilized as a communication interface to computing and mobile devices for low literacy regions.

A Systematic Analysis of Automatic Speech Recognition: An Overview

Abstract: Most high-flying and primary means of communication among humans is speech. Despite the researches and developments in the field of automatic speech recognition the accuracy of the said is still a research challenge. This paper reviews past work comparing modern speech recognition systems and humans to determine how far recent dramatic progress in technology has evolved towards the objective of human-like performance. An overview of sources of knowledge is introduced and the use of knowledge to create and verify hypotheses is discussed.

FPGA based fast Lithuanian isolated word recognition system

Abstract: The article presents the Lithuanian isolated word recognition system implementation in a FPGA hard-core. The pursued objective is the acceleration of the previous soft-core implementation at both key stages: feature extraction and recognition. The 12-th order cepstral analysis is used to extract speech signal features, while for isolated word recognition a dynamic time warping is used. Implementation completely done in the VHDL hard-core allowed us to 320 times speed-up the signal cepstrum calculation and 348 times - one dynamic time warping comparison with border constraints. The recognition system works in real time and is built on medium class FPGA, operating at 50 MHz main clock frequency. It is tested on 6 times repeated 100 Lithuanian words dictionary. Speaker dependent recognition tests done for 10 speakers yield the 97.7 % average recognition accuracy (with 4.9 % recognition improvement over the previous implementation).

Quality Estimation Methodology of Speech Recognition Features

Abstract: The best feature set selection is the key of successful speech recognition system. Quality measure is needed to characterize the chosen feature set. Variety of feature quality metrics are proposed by other authors. However, no guidance is given to choose the appropriate metric. Also no metrics investigations for speech features were made. In the paper the methodology for quality estimation of speech features is presented. Metrics have to be chosen on the ground of their correlation with classification results. Linear Frequency Cepstrum (LFCC), Mel Frequency Cepstrum (MFCC), Perceptual Linear Prediction (PLP) analyses were selected for experiment. The most proper metric was chosen in combination with Dynamic Time Warping (DTW) classifier. Experimental investigation results are presented. Ill. 5, bibl. 18, tabl. 3 (in English; abstracts in English and Lithuanian). http://dx.doi.org/10.5755/j01.eee.110.4.302