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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.
eSpeak is a free and open-source, cross-platform, compact, software speech synthesizer.It uses a formant synthesis method, providing many languages in a relatively small file size. eSpeakNG (Next Generation) is a continuation of the original developer's project with more feedback from native speakers.
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.
Otter.ai, Inc. is an American transcription software company based in Mountain View, California.The company develops speech to text transcription applications using artificial intelligence and machine learning.
Most voice synthesizers (including Apple's Siri) use concatenative synthesis, [5] in which a program stores individual phonemes and then pieces them together to form words and sentences. WaveNet synthesizes speech with human-like emphasis and inflection on syllables, phonemes, and words. Unlike most other text-to-speech systems, a WaveNet model ...
It is necessary to collect clean and well-structured raw audio with the transcripted text of the original speech audio sentence. 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.
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