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20212026
most citedA Comparison of Discrete and Soft Speech Units for Improved Voice Conversion

111 citations · 133 across the 7 of their papers we have counts for

collaborators

7 papers

eess.AS2026

Interpreting Speaker Characteristics in the Dimensions of Self-Supervised Speech Features

Kyle Janse van Rensburg, Benjamin van Niekerk, Herman Kamper

How do speech models trained through self-supervised learning structure their representations? Previous studies have looked at how information is encoded in feature vectors across…

eess.AS2024

Revisiting speech segmentation and lexicon learning with better features

Herman Kamper, Benjamin van Niekerk

We revisit a self-supervised method that segments unlabelled speech into word-like segments. We start from the two-stage duration-penalised dynamic programming method that performs…

eess.AS2023

Rhythm Modeling for Voice Conversion

Benjamin van Niekerk, Marc-André Carbonneau, Herman Kamper

Voice conversion aims to transform source speech into a different target voice. However, typical voice conversion systems do not account for rhythm, which is an important factor in…

eess.AS2023★ 1 cited

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…

cs.CL2023

Visually grounded few-shot word acquisition with fewer shots

Leanne Nortje, Benjamin van Niekerk, Herman Kamper

We propose a visually grounded speech model that acquires new words and their visual depictions from just a few word-image example pairs. Given a set of test images and a spoken qu…

eess.AS2021★ 111 cited

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…

cs.SD2021★ 21 cited

Daft-Exprt: Cross-Speaker Prosody Transfer on Any Text for Expressive Speech Synthesis

Julian Zaïdi, Hugo Seuté, Benjamin van Niekerk +1

This paper presents Daft-Exprt, a multi-speaker acoustic model advancing the state-of-the-art for cross-speaker prosody transfer on any text. This is one of the most challenging, a…