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Found 8 papers, 0 papers with code
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.
Cross-lingual Knowledge Distillation via Flow-based Voice Conversion for Robust Polyglot Text-To-Speech
In the first two stages, we use a VC model to convert utterances in the target locale to the voice of the target speaker.
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.
On granularity of prosodic representations in expressive text-to-speech
In expressive speech synthesis it is widely adopted to use latent prosody representations to deal with variability of the data during training.
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.
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.
