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Found 8 papers, 0 papers with code
AE-Flow: AutoEncoder Normalizing Flow
The results show that the proposed training paradigm systematically improves speaker similarity and naturalness when compared to regular training methods of normalizing flows.
Creating New Voices using Normalizing Flows
As there is growing interest in synthesizing voices of new speakers, here we investigate the ability of normalizing flows in text-to-speech (TTS) and voice conversion (VC) modes to extrapolate from speakers observed during training to create unseen speaker identities.
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space.
Remap, warp and attend: Non-parallel many-to-many accent conversion with Normalizing Flows
Regional accents of the same language affect not only how words are pronounced (i. e., phonetic content), but also impact prosodic aspects of speech such as speaking rate and intonation.
GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion
We evaluate our models in terms of intelligibility, speaker similarity and naturalness for intra- and cross-lingual conversion in seen and unseen languages.
Text-free non-parallel many-to-many voice conversion using normalising flows
We investigate normalising flows for VC in both text-conditioned and text-free scenarios.
Enhancing audio quality for expressive Neural Text-to-Speech
Artificial speech synthesis has made a great leap in terms of naturalness as recent Text-to-Speech (TTS) systems are capable of producing speech with similar quality to human recordings.
Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech
In this paper, we present a method for building highly expressive TTS voices with as little as 15 minutes of speech data from the target speaker.
