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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.
Gnuspeech is an extensible text-to-speech computer software package that produces artificial speech output based on real-time articulatory speech synthesis by rules. That is, it converts text strings into phonetic descriptions, aided by a pronouncing dictionary, letter-to-sound rules, and rhythm and intonation models; transforms the phonetic descriptions into parameters for a low-level ...
DEEP-VOICE [75] is a publicly available dataset intended for research purposes to develop systems to detect when speech has been generated with neural networks through a process called Retrieval-based Voice Conversion (RVC). Preliminary research showed numerous statistically-significant differences between features found in human speech and ...
MBROLA is speech synthesis software as a worldwide collaborative project. The MBROLA project web page provides diphone databases for many [1] spoken languages.. The MBROLA software is not a complete speech synthesis system for all those languages; the text must first be transformed into phoneme and prosodic information in MBROLA's format, and separate software (e.g. eSpeakNG) is necessary.
Speech recognition software is available for many computing platforms, operating systems, use models, and software licenses. Here is a listing of such, grouped in various useful ways. Here is a listing of such, grouped in various useful ways.
Since such inverse autoregressive flow-based models are non-auto-regressive when performing inference, the inference speed is faster than real-time. Meanwhile, Nvidia proposed a flow-based WaveGlow [16] model, which can also generate speech faster than real-time. However, despite the high inference speed, parallel WaveNet has the limitation of ...
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Attention-based ASR models were introduced simultaneously by Chan et al. of Carnegie Mellon University and Google Brain and Bahdanau et al. of the University of Montreal in 2016. [100] [101] The model named "Listen, Attend and Spell" (LAS), literally "listens" to the acoustic signal, pays "attention" to different parts of the signal and "spells ...