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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.
The person on the screen created by DeepBrain AI becomes an announcer who delivers news 24/7 and a YouTube creator who communicates with viewers in real time. Sometimes we imitate people who are familiar to us.
-How deep learning changed the current NFT market- I'm sure the term NFT is not unfamiliar to people who surf the Internet every day, but can you think of the connection between artificial intelligence and NFTs? Can you imagine the next popular artist being AI? Let’s get started!
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