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Voice Conversion Systems

Our unique technology can change your voice to that of another person (e.g., a celebrity) while preserving all the subtle detail of how you say what you say.

About the system

Voice Conversion Systems

We leverage recent breakthroughs in the field of machine learning called deep learning which allow artificial neural networks to produce high quality synthetic speech. Up until now, these techniques have mainly been used for text-to-speech conversion. Because text contains very little prosodic information, this results in a rather monotone output. By doing speech-to-speech, we circumvent this problem, copying the intonation of the input speech to the output.

Why the world needs voice conversion

microphone

Movie dubbing and ADR

With our technology, movies can be dubbed using the voices of the original actors, providing a big wow factor. Relatedly, production companies often need to record short bits of dialog after principal photography (ADR), and with our technology they can fake it when the actor is no longer easily available. Finally, our technology will reinvigorate porn parody films with famous voices.

Famous voices

Famous voices

Any actor can speak with a famous voice. The talent can get ill and even die. But their voice can live on. Audiobooks narrated in the author’s voice. Tribute concerts for singers who have passed away.

Call centers

Call centers

A whole call center can speak with one good voice. It could be the voice of a celebrity or of the business owner. Or call centers can switch between voices, targeting them to customers.

Entertainment and VR

Entertainment and VR

Entertainment such as karaoke, new highly immersive VR games as well as traditional online games will need voices that our technology is poised to provide.

Speech problems

Speech problems

Personalized synthetic voice for people with speech problems.

Samples

These samples are generated by our current prototype trained on the CMU Arctic dataset. A trained system takes a file spoken by a source speaker (“Source” column) and produces a result (“Target, converted” column), as if it was spoken by the target person (“Target, true” column). Note that the true target samples are given here just for comparison; the system only uses the source voice for conversion. Neither the source nor the target samples below were ever seen by the model during training.

Source

Target, converted

Target, true

Source

Target, converted

Target, true

Source

Target, converted

Target, true

Source

Target, converted

Target, true

Source

Target, converted

Target, true

Source

Target, converted

Target, true

Team

Dmytro Bielievtsov

Dmytro Bielievtsov

CTO
Grant Reaber

Grant Reaber

Chief Research Officer
Oleksandr Khapilin

Oleksandr Khapilin

Deep Learning Engineer
Dmytro Danevskyi

Dmytro Danevskyi

Deep Learning Engineer
Anton Shcherbyna

Anton Shcherbyna

Deep Learning Engineer
Oleksandr Serdiuk

Oleksandr Serdiuk

CEO

Contact us

We are always glad to build connections with talented engineers, marketing and sales professionals, investors and, of course, customers.

Please, feel free to write us using this form:

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