DEMOS

MOTION ESTIMATION

FACE TRACKING

NIKHIL’S PAI (ObEN CEO)

ADAM’S PAI (ObEN COO)

AIJIA’S PAI (SNH48 BAND MEMBER)

LUCAS’ PAI (DISCOVERY CHANNEL HOST)

ADRIAN’S PAI (K11 Chairman)

PAI DANCE

VIRTUAL SINGER

Aijia's Original Voice
TTS Voice
PAI Singing
PAI/HUMAN DUET

EXPRESSIVE TTS

Original Voice
TTS Voice
TTS Happy
TTS Angry
TTS Sad

OUR LATEST PUBLICATIONS

Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention

Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been applied to numerous domains, such as data augmentation, domain adaptation and unsupervised training. When paired training data is not accessible, image translation...

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A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment

Voice conversion (VC) aims at conversion of speaker characteristic without altering content. Due to training data limitations and modeling imperfections, it is difficult to achieve believable speaker mimicry without introducing processing artifacts; performance assessment of VC, therefore, usually involves both speaker similarity and quality...

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The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods

We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems. The objective of the challenge was to perform speaker conversion (i.e.\ transform the vocal identity) of a source speaker to...

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ESTHER : Extremely Simple Image Translation Through Self-Regularization

Image translation between two domains is a class of problems where the goal is to learn the mapping from an input image in the source domain to an output image in the target domain. It has important applications such as data augmentation, domain adaptation, and unsupervised training. When paired training data are not accessible, the mapping...

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Investigation of using disentangled and interpretable representations for one-shot cross-lingual voice conversion

This blog post presents a one-shot voice conversion technique, in which a variational autoencoder (VAE) is used to disentangle speech factors. We show that VAEs are able to disentangle the speaker identity and linguistic content from speech acoustic features. Modification of these factors allow transformation of voice. We show that the...

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One-shot Voice Conversion using Variational Autoencoders

This blog post presents a one-shot voice conversion technique, in which a variational autoencoder (VAE) is used to disentangle speech factors. We show that VAEs are able to disentangle the speaker identity and linguistic content from speech acoustic features. Modification of these factors allow transformation of voice. We show that the...

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Voice Approximation for Inter-Gender Voice Personalization

One of the key technologies at ObEN is the personalization of voice identity, consisting of a transformation of an input voice (e.g., from a Text-To-Speech system) to render it perceptually similar to a target one (e.g., a celebrity, or a user’s voice). Although some existing technologies, known as Voice Conversion and based on a statistical...

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ObEN is an artificial intelligence company that is building a decentralized AI platform for Personal AI (PAI), intelligent 3D avatars that look, sound, and behave like the individual user. Deployed on the Project PAI blockchain, ObEN’s technology enables users to create, use, and manage their own PAI on a secure, decentralized platform - enabling never before possible social and virtual interactions. Founded in 2014, ObEN is a K11, Tencent, Softbank Ventures Korea and HTC Vive X portfolio company and is located at Idealab in Pasadena, California.


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