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Talking Points: The Role of Computer Voice in the Future of Speech-Based Human-Computer Interaction

Published: June 9, 2021 In the modern day, our interactions with voice-based devices and services continue to increase. In this light, researchers at Tokyo Institute of Technology and RIKEN, Japan, have performed a meta-synthesis to understand how we perceive and interact with the voice (and the body) of various machines. Their findings have generated insights into human preferences, and can be used by engineers and designers to develop future vocal technologies. As humans, we primarily communicate vocally and aurally. We convey not just linguistic information, but also the complexities of our emotional states and personalities. Aspects of our voice such as tone, rhythm, and pitch are vital to the way we are perceived. In other words, the way we say things matters.

Engineers create a programmable fiber

Massachusetts Institute of Technology MIT researchers have created the first fiber with digital capabilities, able to sense, store, analyze, and infer activity after being sewn into a shirt. Yoel Fink, who is a professor of material sciences and electrical engineering, a Research Laboratory of Electronics principal investigator, and the senior author on the study, says digital fibers expand the possibilities for fabrics to uncover the context of hidden patterns in the human body that could be used for physical performance monitoring, medical inference, and early disease detection. Or, you might someday store your wedding music in the gown you wore on the big day – more on that later.

Scientists find molecular patterns that may help identify extraterrestrial life

Published: June 1, 2021 Scientists have begun the search for extraterrestrial life in the Solar System in earnest, but such life may be subtly or profoundly different from Earth-life, and methods based on detecting particular molecules as biosignatures may not apply to life with a different evolutionary history. A new study by a joint Japan/US-based team, led by researchers at the Earth-Life Science Institute (ELSI) at the Tokyo Institute of Technology, has developed a machine learning technique which assesses complex organic mixtures using mass spectrometry to reliably classify them as biological or abiological. Figure 1. It is not life as we know or understand it.

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