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Found 13 papers, 3 papers with code
Robust universal neural vocoding
This paper introduces a robust universal neural vocoder trained with 74 speakers (comprised of both genders) coming from 17 languages.
Towards achieving robust universal neural vocoding
This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker or style seen during training or from an out-of-domain scenario when the recording conditions are studio-quality.
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data
Neural text-to-speech synthesis (NTTS) models have shown significant progress in generating high-quality speech, however they require a large quantity of training data.
Effect of data reduction on sequence-to-sequence neural TTS
Recent speech synthesis systems based on sampling from autoregressive neural networks models can generate speech almost undistinguishable from human recordings.
Low-resource expressive text-to-speech using data augmentation
While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style.
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.
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.
Expressive, Variable, and Controllable Duration Modelling in TTS
First, we propose a duration model conditioned on phrasing that improves the predicted durations and provides better modelling of pauses.
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.
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.
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.
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.
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.
