activity
20162023
most citedUnsupervised neural and Bayesian models for zero-resource speech processing

6 citations · 9 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL20231 cited

Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili

Christiaan Jacobs, Nathanaël Carraz Rakotonirina, Everlyn Asiko Chimoto +2

We consider hate speech detection through keyword spotting on radio broadcasts. One approach is to build an automatic speech recognition (ASR) system for the target low-resource la…

eess.AS20231 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…

cs.CL2023

Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learning

Ruan van der Merwe, Herman Kamper

We consider the problem of few-shot spoken word classification in a setting where a model is incrementally introduced to new word classes. This would occur in a user-defined keywor…

eess.AS20221 cited

A Temporal Extension of Latent Dirichlet Allocation for Unsupervised Acoustic Unit Discovery

Werner van der Merwe, Herman Kamper, Johan du Preez

Latent Dirichlet allocation (LDA) is widely used for unsupervised topic modelling on sets of documents. No temporal information is used in the model. However, there is often a rela…

cs.CL20176 cited

Unsupervised neural and Bayesian models for zero-resource speech processing

Herman Kamper

In settings where only unlabelled speech data is available, zero-resource speech technology needs to be developed without transcriptions, pronunciation dictionaries, or language mo…