In this paper, we are analyzing the results of native Lithuanian speaker recognition and identification using long short-term memory deep neural network. We look at recognition accuracy and identify further potential improvements. Dataset used for training and speaker recognition consists of over 370 unique speakers, who provide their voice utterances in Lithuanian language. In this paper we present results that are derived from part of this dataset.
This work put forwards an optimal BCI (Brain Computer Interface) speller design based on Steady State Visual Evoked Potentials (SSVEP) and Artificial Neural Network (ANN) in order to help the people with severe motor impairments. This work is carried out to enhance the accuracy and communication rate of BCI system. To optimize the BCI system, the work has been divided into two steps: First, designing of an encoding technique to choose characters from the speller interface and the second is the development and implementation of feature extraction algorithm to acquire optimal features, which is used to train the BCI system for classification using neural network. Optimization of speller interface is focused on representation of character matrix and its designing parameters. Then again, a lot of deliberations made in order to optimize selection of features and user’s time window. Optimized system works nearly the same with the new user and gives character per minute (CPM) of 13 ± 2 with an average accuracy of 94.5% by choosing first two harmonics of power spectral density as the feature vectors and using the 2 second time window for each selection. Optimized BCI performs better with experienced users with an average accuracy of 95.1%. Such a good accuracy has not been reported before in account of fair enough CPM.