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Found 8 papers, 5 papers with code
Voice Conversion for Stuttered Speech, Instruments, Unseen Languages and Textually Described Voices
Nevertheless, this shows that voice conversion models - and kNN-VC in particular - are increasingly applicable in a range of non-standard downstream tasks.
Disentanglement in a GAN for Unconditional Speech Synthesis
We confirm that ASGAN's latent space is disentangled: we demonstrate how simple linear operations in the space can be used to perform several tasks unseen during training.
Voice Conversion With Just Nearest Neighbors
Any-to-any voice conversion aims to transform source speech into a target voice with just a few examples of the target speaker as a reference.
Ranked #1 on
Voice Conversion
on LibriSpeech test-clean
(using extra training data)
TransFusion: Transcribing Speech with Multinomial Diffusion
In this work we aim to see whether the benefits of diffusion models can also be realized for speech recognition.
GAN You Hear Me? Reclaiming Unconditional Speech Synthesis from Diffusion Models
As in the StyleGAN family of image synthesis models, ASGAN maps sampled noise to a disentangled latent vector which is then mapped to a sequence of audio features so that signal aliasing is suppressed at every layer.
Voice Conversion Can Improve ASR in Very Low-Resource Settings
In this work we assess whether a VC system can be used cross-lingually to improve low-resource speech recognition.
Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing
In this paper, we first show that the per-utterance mean of CPC features captures speaker information to a large extent.
StarGAN-ZSVC: Towards Zero-Shot Voice Conversion in Low-Resource Contexts
We specifically extend the recent StarGAN-VC model by conditioning it on a speaker embedding (from a potentially unseen speaker).
