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This study presented the first light curve analysis of the OP Boo and V0511 Cam binary stars, which was conducted in the frame of the Binary Systems of South and North (BSN) Project. Photometric ground-based observations were conducted with standard filters at two observatories in the Czech Republic. We computed a new ephemeris for each of the systems using our extracted times of minima, TESS data, and additional literature. Linear fits for O-C diagrams of both systems were considered using the Markov Chain Monte Carlo (MCMC) method. The light curves were analyzed using the Wilson-Devinney (WD) binary code combined with the Monte Carlo (MC) simulation. The light curve solutions of both target systems required a cold starspot. The absolute parameters of the systems were calculated by using a P-M parameter relationship. The positions of the systems were also depicted on the Hertzsprung-Russell (HR), P-L, logMtot-logJ0, and T-M diagrams. The second component in both systems is determined to be a more massive and hotter star. Therefore, it can be concluded that both systems are W-type contact binary systems., Comment: 5 Tables, 4 Figures, Astrophysics Journal

The light curve analyses and orbital period variations for two contact binary stars, LS Del and V997 Cyg, were presented in this work which was conducted in the frame of the Binary Systems of South and North (BSN) Project. Ground-based photometric observations were performed at two observatories in France. We used the TESS (Transiting Exoplanet Survey Satellite) data for extracting times of minima and light curve analysis of the target systems. The O-C diagram for both systems displays a parabolic trend. LS Del and V997 Cyg's orbital periods are increasing at a rate of dP/dt= 7.20*10^{-08} d yr^{-1} and dP/dt= 2.54*10^{-08} d yr^{-1}, respectively. Therefore, it can be concluded that the mass is being transferred from the less massive star to the more massive component with a rate of dM/dt= -1.96*10^{-7} M_Sun yr^{-1} for the LS Del system, and dM/dt= -3.83*10^{-7} M_Sun yr^{-1} for V997 Cyg. The parameters of the third possible object in the system were also considered. The PHysics Of Eclipsing BinariEs (PHOEBE) Python code was used to analyze the light curves. The light curve solutions needed a cold starspot due to the asymmetry in the LS Del system's light curve maxima. The mass ratio, fill-out factor, and star temperature all indicate that both systems are contact binary types in this investigation. Two methods were used to estimate the absolute parameters of the systems: one method used the parallax of Gaia DR3, and the other used a P-M relationship. The positions of the systems were also depicted on the M-L, M-R, q-L_{ratio}, and logM_{tot}-logJ_0 diagrams. We recommended that further observations and investigations be done on the existence of the fourth body in this system., Comment: Accepted by the RAA Journal

The first multiband photometric solutions of the short-period V Gru eclipsing binary from the southern hemisphere is presented in this study. Light curves of the system were observed through BVI filters at the Congarinni Observatory in Australia for 15 nights. In addition to the new ground-based data, we also used the TESS observations in two sectors. We analyzed the light curves of the system using the PHysics Of Eclipsing BinariEs (PHOEBE) 2.4.7 version code to achieve the best accordance with the photometric observations. The solutions suggest that V Gru is a near-contact binary system with q=1.302(81) mass ratio, f1=0.010(23), f2=-0.0.009(21), and i=73.45(38). We considered the two hot spots on the hotter and cooler components for the light curve analysis. We extracted the minima times from the light curves based on the Markov Chain Monte Carlo (MCMC) approach. Using our new light curves, TESS, and additional literature minima, we computed the ephemeris of V Gru. The system's eclipse timing variation trend was determined using the MCMC method. This system is a good and challenging case for future studies., Comment: Accepted by the Research in Astronomy and Astrophysics (RAA) journal

Non-native speech causes automatic speech recognition systems to degrade in performance. Past strategies to address this challenge have considered model adaptation, accent classification with a model selection, alternate pronunciation lexicon, etc. In this study, we consider a recurrent neural network (RNN) with connectionist temporal classification (CTC) cost function trained on multi-accent English data including US (Native), Indian and Hispanic accents. We exploit dark knowledge from a model trained with the multi-accent data to train student models under the guidance of both a teacher model and CTC cost of target transcription. We show that transferring knowledge from a single RNN-CTC trained model toward a student model, yields better performance than the stand-alone teacher model. Since the outputs of different trained CTC models are not necessarily aligned, it is not possible to simply use an ensemble of CTC teacher models. To address this problem, we train accent specific models under the guidance of a single multi-accent teacher, which results in having multiple aligned and trained CTC models. Furthermore, we train a student model under the supervision of the accent-specific teachers, resulting in an even further complementary model, which achieves +20.1% relative Character Error Rate (CER) reduction compared to the baseline trained without any teacher. Having this effective multi-accent model, we can achieve further improvement for each accent by adapting the model to each accent. Using the accent specific model's outputs to regularize the adapting process (i.e., a knowledge distillation version of Kullback-Leibler (KL) divergence) results in even superior performance compared to the conventional approach using general teacher models., Comment: Accepted at SLT 2018

This study presents systems submitted by the University of Texas at Dallas, Center for Robust Speech Systems (UTD-CRSS) to the MGB-3 Arabic Dialect Identification (ADI) subtask. This task is defined to discriminate between five dialects of Arabic, including Egyptian, Gulf, Levantine, North African, and Modern Standard Arabic. We develop multiple single systems with different front-end representations and back-end classifiers. At the front-end level, feature extraction methods such as Mel-frequency cepstral coefficients (MFCCs) and two types of bottleneck features (BNF) are studied for an i-Vector framework. As for the back-end level, Gaussian back-end (GB), and Generative Adversarial Networks (GANs) classifiers are applied alternately. The best submission (contrastive) is achieved for the ADI subtask with an accuracy of 76.94% by augmenting the randomly chosen part of the development dataset. Further, with a post evaluation correction in the submitted system, final accuracy is increased to 79.76%, which represents the best performance achieved so far for the challenge on the test dataset.

This paper investigates the application of the probabilistic linear discriminant analysis (PLDA) to speaker diarization of telephone conversations. We introduce using a variational Bayes (VB) approach for inference under a PLDA model for modeling segmental i-vectors in speaker diarization. Deterministic annealing (DA) algorithm is imposed in order to avoid local optimal solutions in VB iterations. We compare our proposed system with a well-known system that applies k-means clustering on principal component analysis (PCA) coefficients of segmental i-vectors. We used summed channel telephone data from the National Institute of Standards and Technology (NIST) 2008 Speaker Recognition Evaluation (SRE) as the test set in order to evaluate the performance of the proposed system. We achieve about 20% relative improvement in Diarization Error Rate (DER) compared to the baseline system.