4 accepted papers
Deep neural network based deepfake detection systems have achieved high levels of accuracy on benchmark datasets and competitions. However, most models lack interpretability. It is challenging to extract reasoning from the network that can convince the human evaluator to trust the decision. Humans o…
End-to-end (E2E) Automatic Speech Recognition (ASR) systems are widely applied in various devices and communication domains. However, state-of-the-art ASR systems are known to underperform when there is a mismatch in the training and test domains. As a result, acoustic models deployed in production…
Training discriminative classifiers involves learning a conditional distribution p(y <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> |x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ), giv…
Articulatory information can effectively model variability in speech and can improve speech recognition performance under varying acoustic conditions. Learning speaker-independent articulatory models has always been challenging, as speaker-specific information in the articulatory and acoustic spaces…