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DeepBrain AI completed the AWS Foundational Technical Review (FTR)
The new studio is about three times larger than the existing Chroma Key Room, and the height ceiling has been raised, making it easy to take full-body shots of the model. By reducing the distortion of the model's body proportion, it has become easier to produce higher-quality AI humans in the future.
Hello, I'm Nam Kyu-hyun, a researcher at Deep Brain AI's Deep-Learning Team. Our team studies and develops technology that goes into creating AI Humans. Among various technologies, I’m mainly in charge of voice synthesis system. In addition, I also conduct research regarding NLP on Chatbots.
To dive deeper into this space, I recently sat down with Joe Murphy, who leads US business development for DeepBrainAI, to explore the business potential of virtual humans, examine the technologies that enable them, and the many challenges ahead as this approach to human-machine interaction matures and scales.
Hello, I'm Park Young-hoon, the creative part of Deep Brain AI.
We're going to make an Weekly Meeting Video using AI STUDIOS.
Like the AdaSpeech model we looked at last time, the existing TTS adaptation method has used text-speech pair data to synthesize the voices of a specific speaker. However, since it is practically difficult to prepare data in pairs, it will be a much more efficient way to adapt the TTS model only with speech data that is not transcribed. The easiest way to access is to use the automatic speech recognition (ASR) system for transcription, but it is difficult to apply in certain situations and recognition accuracy is not high enough, which can reduce final adaptation performance. And there have been attempts to solve this problem by joint training of the TTS pipeline and the module for adaptation, which has the disadvantage of not being able to easily combine with other commercial TTS models.
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