Tuesday January 21, 2020
Home Lead Story Thanks To Art...

Thanks To Artificial Intelligence, Radio Journalist Regains His Voice

The AI system slices each word read out by an individual into 100 tiny pieces

0
//
AI scenarios present ethical issues ranging from privacy, human rights, employment or other social issues.
The AI-based system named "Philyra" can learn about perfume formulas, raw materials, historical success data and industry trends. Pixabay

A US radio journalist who had lost his voice two years ago due to a rare neurological condition has regained the ability to speak, thanks to artificial intelligence (AI), the media reported.

Jamie Dupree, 54, a political radio journalist with Cox Media Group, got a new voice that trained a neural network to predict how he would talk, using samples from his old voice recordings, the BBC reported.

With his new voice, Dupree can now write a script and then use a free text-to-speech software programme called Balabolka on his laptop to turn it into an audio recording.

If a word or turn of phrase does not sound quite right in the recording, he can slow certain consonants or vowels down, or swap a word to one that does work, or change the pitch, and he can have a full radio story ready to go live in just seven minutes.

“This has saved my job and saved my family from a terrible financial unknown,” Dupree was quoted as saying to the BBC.

In 2016, Dupree was diagnosed with tongue protrusion dystonia — a rare neurological condition where the tongue pushes forward out of his mouth and his throat tightens whenever he wants to speak, making it impossible for him to say more than two or three words at a time.

artificial intelligence, brain
artificial intelligence, brain, Pixabay

Thanks to the new computer-generated voice, created for him by Scottish technology company CereProc, Dupree is set to come back on air, the report said.

The AI system slices each word read out by an individual into 100 tiny pieces, and does this with lots of common words until eventually it understands how basic phonetics work in that person’s voice and has an ordered sequence for all the pieces in each word.

Then, the neural network can create its own sounds and predict what the person would sound like if they were to say a series of words in conversation.

Also read: This Way China Can Help India In The Terms of Artificial Intelligence

“AI techniques work quite well on small constrained problems, and learning to model speech is something deep neural nets can do really well,” Chris Pidcock, CereProc’s chief technical officer and co-founder, told the BBC. (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)