Overview of BTAS 2016 Speaker Anti-spoofing Competition

This paper provides an overview of the Speaker Anti-spoofing Competition organized by Biometric group at Idiap Research Institute for the IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 2016). The competition used AVspoof database, which contains a comprehensive set of presentation attacks, including, (i) direct replay attacks when a genuine data is played back using a laptop and two phones (Samsung Galaxy S4 and iPhone 3G), (ii) synthesized speech replayed with a laptop, and (iii) speech created with a voice conversion algorithm, also replayed with a laptop. The paper states competition goals, describes the database and the evaluation protocol, discusses solutions for spoofing or presentation attack detection submitted by the participants, and presents the results of the evaluation

The I4U Mega Fusion and Collaboration for NIST Speaker Recognition Evaluation 2016

The 2016 speaker recognition evaluation (SRE'16) is the latest edition in the series of benchmarking events conducted by the National Institute of Standards and Technology (NIST). I4U is a joint entry to SRE'16 as the result from the collaboration and active exchange of information among researchers from sixteen Institutes and Universities across 4 continents. The joint submission and several of its 32 sub-systems were among top-performing systems. A lot of efforts have been devoted to two major challenges, namely, unlabeled training data and dataset shift from Switchboard-Mixer to the new Call My Net dataset. This paper summarizes the lessons learned, presents our shared view from the sixteen research groups on recent advances, major paradigm shift, and common tool chain used in speaker recognition as we have witnessed in SRE'16. More importantly, we look into the intriguing question of fusing a large ensemble of sub-systems and the potential benefit of large-scale collaboration.Peer reviewe

Overview of BTAS 2016 Speaker Anti-spoofing Competition

This paper provides an overview of the Speaker Anti-spoofing Competition organized by Biometric group at Idiap Research Institute for the IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 2016). The competition used AVspoof database, which contains a comprehensive set of presentation attacks, including, (i) direct replay attacks when a genuine data is played back using a laptop and two phones (Samsung Galaxy S4 and iPhone 3G), (ii) synthesized speech replayed with a laptop, and (iii) speech created with a voice conversion algorithm, also replayed with a laptop. The paper states competition goals, describes the database and the evaluation protocol, discusses solutions for spoofing or presentation attack detection submitted by the participants, and presents the results of the evaluation

An Improved Signal Subspace Algorithm for Speech Enhancement

Part 1: Digital ServicesInternational audienceMost of the algorithms for speech enhancement are designed to improve the speech listening comfort. However the frequency spectrum character is destroyed seriously after the speech enhancement. To achieve better speech listening comfort with less frequency spectral damages, we present an improved signal subspace algorithm for speech enhancement. Compared with the traditional signal space method, the improved algorithm can decrease the Mel-frequency Cepstral Coefficients (MFCC) distance, an evaluation measure which means less frequency spectral damages to the voice and keep the voices’ intelligence at the same time. Besides, the method can enlarge the distance of the easily confused voices, which means the improvement of the voice recognition ratio. Thus we get the purpose of the speech enhancement. The improved algorithm is used in a speech recognition program and has a good performance

How to construct perfect and worse-than-coin-flip spoofing countermeasures:a word of warning on shortcut learning

Abstract Shortcut learning, or ‘Clever Hans effect’ refers to situations where a learning agent (e.g., deep neural networks) learns spurious correlations present in data, resulting in biased models. We focus on finding shortcuts in deep learning based spoofing countermeasures (CMs) that predict whether a given utterance is spoofed or not. While prior work has addressed specific data artifacts, such as silence, no general normative framework has been explored for analyzing shortcut learning in CMs. In this study, we propose a generic approach to identifying shortcuts by introducing systematic interventions on the training and test sides, including the boundary cases of ‘near-perfect’ and ‘worse than coin flip’ (label flip). By using three different models, ranging from classic to state-of-the-art, we demonstrate the presence of shortcut learning in five simulated conditions. We also analyze the results using a regression model to understand how biases affect the class-conditional score statistics

Original Articles Predictors of Mortality in Ventilated Neonates in Intensive Care Unit

Background: A large number of neonates in intensive care unit require mechanical ventilation due to various conditions and have a high mortality. To reduce the high mortality in this group of neonates, identification of risk factors is important. Objective: This study was undertaken to find out the predictors of mortality in ventilated neonates in the Intensive Care Unit. Methods: This study was carried out in the Intensive Care Unit of Dhaka Shish

Plasma alpha-2-macroglobulin level in moderate to severe psoriasis

Psoriasis is a T-cell mediated chronic inflammatory diseases where pro-inflammatory mediators are involved in its pathogenesis. Alpha-2-macroglobulin (α-2M) is a panproteinase inhibitor having unique clearing role of different cytokines. This study was conducted on 30 patients with moderate to severe psoriasis to see the plasma level of α-2M and was compared with the normal healthy controls. Patients who were already selected for systemic treatment (methotrexate) and consented for routine blood test for monitoring at baseline and 12 weeks after treatment were enrolled along with 10 healthy controls. The venous blood (5 mL) was collected and the plasma alfa-2 macroglobulin was estimated using the enzyme-linked immunosorbent assay. The mean  plasma α-2M level was 3.0 ± 0.4 g/L among the normal healthy persons, and 2.8 ± 0.7 g/L among the untreated patients of psoriasis (p>0.05). Its level among the patients with psoriasis after systemic antipsoriatic drugs was 2.8 ± 0.6 g/L which was not significantly different from the baseline level (p>0.05). The study shows that the plasma α-2M  level in psoriasis is not different comparing with normal healthy persons