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Deep learning speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech) or spectrum . Deep neural networks are trained using large amounts of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input text.
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Voice-to-text and text-to-voice: Siri can transcribe spoken words into and text as well as read text typed by the user out loud. [95] [96] Text commands: Users can type what they want Siri to do. [97] Personal voice: This allows users to create a synthesized voice that sounds like them. [98]
Retrieval-based Voice Conversion (RVC) is an open source voice conversion AI algorithm that enables realistic speech-to-speech transformations, accurately preserving the intonation and audio characteristics of the original speaker.
Specifically, the transcribed text with the target speaker's voice is the input of the generation model. The text analysis module processes the input text and converts it into linguistic features. Then, the acoustic module extracts the parameters of the target speaker from the audio data based on the linguistic features generated by the text ...
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[1] [2] Compared to Siri, the software's platform is open and can accommodate external plug-ins written to work with the assistant. It can also handle more complex queries. [ 3 ] The development team has been working on the software since 2012 [ 4 ] [ 5 ] and had raised over $22 million in funding by early 2015 [ 6 ] and $30 million by early 2016.
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