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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.
Second, the Text-To-Speech model must be trained using these data to build a synthetic audio generation model. 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.
This real-time capability marks a significant advancement over previous AI voice conversion technologies, such as So-vits SVC. Its speed and accuracy have led many to note that its generated voices sound near-indistinguishable from "real life", provided that sufficient computational specifications and resources (e.g., a powerful GPU and ample ...
ElevenLabs is primarily known for its browser-based, AI-assisted text-to-speech software, Speech Synthesis, which can produce lifelike speech by synthesizing vocal emotion and intonation. [10] The company states that its models are trained to interpret the context in the text, and adjust the intonation and pacing accordingly. [ 11 ]
Backed by $40 million from a16z, Alexis Conneau’s new startup Waveforms is taking his work on OpenAI's voice mode to the next level. Ilya Sutskever hired him to create ChatGPT’s voice at OpenAI.
Work to personalize a synthetic voice to better match a person's personality or historical voice is becoming available. [94] A noted application, of speech synthesis, was the Kurzweil Reading Machine for the Blind which incorporated text-to-phonetics software based on work from Haskins Laboratories and a black-box synthesizer built by Votrax. [95]
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