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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. [1]
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 ...
A stack of dilated casual convolutional layers used in WaveNet [1]. In September 2016, DeepMind proposed WaveNet, a deep generative model of raw audio waveforms, demonstrating that deep learning-based models are capable of modeling raw waveforms and generating speech from acoustic features like spectrograms or mel-spectrograms.
Articulatory synthesis refers to computational techniques for synthesizing speech based on models of the human vocal tract and the articulation processes occurring there. The shape of the vocal tract can be controlled in a number of ways which usually involves modifying the position of the speech articulators, such as the tongue , jaw , and lips.
MBROLA software uses MBROLA (Multi-Band Resynthesis OverLap Add) [3] algorithm for speech generation. Although it is diphone-based, the quality of MBROLA's synthesis is considered to be higher than that of most diphone synthesisers as it preprocesses the diphones imposing constant pitch and harmonic phases that enhances their concatenation while only slightly degrading their segmental quality.
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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 ...