111 citations · 113 across the 8 of their papers we have counts for
8 papers
MARS6: A Small and Robust Hierarchical-Codec Text-to-Speech Model
Matthew Baas, Pieter Scholtz, Arnav Mehta +3
Codec-based text-to-speech (TTS) models have shown impressive quality with zero-shot voice cloning abilities. However, they often struggle with more expressive references or comple…
Voice Conversion for Stuttered Speech, Instruments, Unseen Languages and Textually Described Voices
Matthew Baas, Herman Kamper
Voice conversion aims to convert source speech into a target voice using recordings of the target speaker as a reference. Newer models are producing increasingly realistic output.…
Disentanglement in a GAN for Unconditional Speech Synthesis
Matthew Baas, Herman Kamper
Can we develop a model that can synthesize realistic speech directly from a latent space, without explicit conditioning? Despite several efforts over the last decade, previous adve…
Voice Conversion With Just Nearest Neighbors
Matthew Baas, Benjamin van Niekerk, Herman Kamper
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. Recent methods produce convincing con…
TransFusion: Transcribing Speech with Multinomial Diffusion
Matthew Baas, Kevin Eloff, Herman Kamper
Diffusion models have shown exceptional scaling properties in the image synthesis domain, and initial attempts have shown similar benefits for applying diffusion to unconditional t…
GAN You Hear Me? Reclaiming Unconditional Speech Synthesis from Diffusion Models
Matthew Baas, Herman Kamper
We propose AudioStyleGAN (ASGAN), a new generative adversarial network (GAN) for unconditional speech synthesis. As in the StyleGAN family of image synthesis models, ASGAN maps sam…
Voice Conversion Can Improve ASR in Very Low-Resource Settings
Matthew Baas, Herman Kamper
Voice conversion (VC) could be used to improve speech recognition systems in low-resource languages by using it to augment limited training data. However, VC has not been widely us…
A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion
Benjamin van Niekerk, Marc-André Carbonneau, Julian Zaïdi +3
The goal of voice conversion is to transform source speech into a target voice, keeping the content unchanged. In this paper, we focus on self-supervised representation learning fo…