Abstract Adversarial attacks deceive neural network systems by adding carefully crafted perturbations to benign signals. Being almost imperceptible to humans, these attacks pose a severe security threat to the state-of-the-art speech and speaker recognition systems, making[…]

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Abstract Since it is increasingly harder to opt out from interacting with AI technology, people demand that AI is capable of maintaining contracts such that it supports agency and oversight of people who are required[…]

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Abstract Enormous amounts of ever-changing  knowledge are available online in diverse textual styles and diverse formats. Recent advances in deep learning algorithms and large-scale datasets are spurring progress in many Natural Language Processing (NLP) tasks,[…]

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Abstract While rapid advances in technology and data availability have greatly increased the practical usability of natural language processing (NLP) models, current failures to center people in NLP research has contributed to an ethical crisis:[…]

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Abstract As AI-driven language interfaces (such as chat-bots) become more integrated into our lives, they need to become more versatile and reliable in their communication with human users. How can we make progress toward building[…]

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Abstract As humans, our understanding of language is grounded in a rich mental model about “how the world works” – that we learn through perception and interaction. We use this understanding to reason beyond what[…]

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Abstract Automatic discovery of phone or word-like units is one of the core objectives in zero-resource speech processing. Recent attempts employ contrastive predictive coding (CPC), where the model learns representations by predicting the next frame[…]

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