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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.
Modelling low-resource accents without accent-specific TTS frontend
Prior work on modelling accents assumes a phonetic transcription is available for the target accent, which might not be the case for low-resource, regional accents.
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.
Unify and Conquer: How Phonetic Feature Representation Affects Polyglot Text-To-Speech (TTS)
In this paper, we conduct a comprehensive study comparing multilingual NTTS systems models trained with both representations.
Mix and Match: An Empirical Study on Training Corpus Composition for Polyglot Text-To-Speech (TTS)
Training multilingual Neural Text-To-Speech (NTTS) models using only monolingual corpora has emerged as a popular way for building voice cloning based Polyglot NTTS systems.
Singing Synthesis: with a little help from my attention
We present UTACO, a singing synthesis model based on an attention-based sequence-to-sequence mechanism and a vocoder based on dilated causal convolutions.
