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Speech research is remarkable in so many ways – in its essential human-centeredness, the rich interconnections between the science and technology, and its wide-ranging impact that is both fundamental and applied. Crucial advances in speech science research catalyze and leverage technological advances across the machine intelligence ecosystem, from sensing and imaging to signal processing and machine learning. Likewise, creation of speech-centric societal applications benefits from an understanding of how humans produce, process and use speech in communication. In these complementary endeavors, two intertwined lines of inquiry endure: illuminating the rich information tapestry and inherent variability in speech and creating trustworthy speech technologies.
AI can extend human capabilities but requires addressing challenges in education, jobs, and biases. Taking a responsible approach involves understanding AI’s nature, design choices, societal role, and ethical considerations. Recent AI developments, including foundational models, transformer models, generative models, and large language models (LLMs), raise questions about whether they are changing the paradigm of AI, and about the responsibility of those that are developing and deploying AI systems. In all these developments, is vital to understand that AI is not an autonomous entity but rather dependent on human responsibility and decision-making.
In this talk I will explore why and how intonational rises are used to orient attention towards the words and phrases bearing them. The attention orienting function of rising pitch is known outside the linguistic domain, with evidence from auditory looming, a phenomenon whereby a signal that increases in loudness or pitch appears to be approaching the listener and is perceived as an immediate threat.