Tuesday January 21, 2020
Home Lead Story New AI-Enable...

New AI-Enabled Software May Translate Thoughts Into Speech

Mesgarani and his team planned to test more complicated words and to run the same tests on brain signals emitted when a person speaks or imagines speaking.

0
//
Artificial Intelligence Bot
Artificial Intelligence Bot. Pixabay

US engineers developed an Artificial Intelligence (AI)-enabled
system that can translate brain signals into intelligible speech, a breakthrough that may help those who cannot speak to communicate with the outside world.

The study, led by Columbia University researchers, showed that by monitoring one’s brain activity, an AI-enabled technology can reconstruct words a person hears with unprecedented clarity, Xinhua news agency reported.

A team of neuroscientists from the varsity trained a voice synthesiser or vocoder to measure brain activity patterns of epilepsy patients already undergoing surgery while those patients listened to sentences spoken by different people.

AI has the potential to increase India's annual growth.
AI has the potential to increase India’s annual growth. pixabay

Those patients listened to speakers reading digits between zero to nine while recording brain signals via the vocoder.

Then, they used a neural network, a type of artificial intelligence, to analyse the signals, and gave robotic-sounding voices, according to the study published in the journal Scientific Reports.

“We found that people could understand and repeat the sounds about 75 per cent of the time, which is well above and beyond any previous attempts,” said Nima Mesgarani from the varsity.

Artificial intelligence
The study, led by Columbia University researchers, showed that by monitoring one’s brain activity, an AI-enabled technology can reconstruct words a person hears with unprecedented clarity Artificial intelligence, wikimedia

Also Read: JLF Panel Examine The Future Of AI

Previous research showed that when people speak or even imagine speaking, distinct patterns of activity take place in their brain and those pattern of signals also emerge when we listen to someone speak or imagine listening.

Mesgarani and his team planned to test more complicated words and to run the same tests on brain signals emitted when a person speaks or imagines speaking.

Mesgarani called it a “game changer” that may give anyone who has lost their ability to speak a new chance to connect to the outside world. (IANS)

Next Story

This AI can Detect Low-Sugar Levels Without any Fingerprick Tests

AI can spot low-glucose levels without fingerprick test

0
Sugar test
Current methods to measure sugarrequires needles and repeated fingerpicks over the day. Pixabay

Researchers have developed a new Artificial Intelligence (AI)-based technique that can detect low-sugar levels from raw ECG signals via wearable sensors without any fingerprint test.

Current methods to measure glucose requires needles and repeated fingerpicks over the day. Fingerpicks can often be painful, deterring patient compliance.

The new technique developed by researchers at University of Warwick works with an 82 per cent reliability, and could replace the need for invasive finger-prick testing with a needle, especially for kids who are afraid of those.

Sugar test
Fingerpicks are never pleasant for a sugar-level test and in some circumstances are particularly cumbersome. Pixabay

“Our innovation consisted in using AI for automatic detecting hypoglycaemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping,” said Dr Leandro Pecchia from School of Engineering in a paper published in the Nature Springer journal Scientific Reports.

Two pilot studies with healthy volunteers found the average sensitivity and specificity approximately 82 per cent for hypoglycaemia detection.

“Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpick during the night certainly is unpleasant, especially for patients in paediatric age,” said Pecchia.

Also Read- Researchers Find a New Mechanism to Prevent Obesity

The figure shows the output of the algorithms over the time: the green line represents normal glucose levels, while the red line represents the low glucose levels.

“Our approach enable personalised tuning of detection algorithms and emphasise how hypoglycaemic events affect ECG in individuals. Basing on this information, clinicians can adapt the therapy to each individual,” the authors wrote. (IANS)