299 citations · 646 across the 28 of their papers we have counts for
12 papers · 1 filter
From Birdsong to Rumbles: Classifying Elephant Calls with Out-of-Species Embeddings
Christiaan M. Geldenhuys, Thomas R. Niesler
We show that pretrained acoustic embeddings classify elephant vocalisations at a level approaching that of end-to-end supervised neural networks, without any fine-tuning of the emb…
Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings
Julian Herreilers, Christiaan Jacobs, Thomas Niesler
We introduce a new approach, the ContrastiveTransformer, that produces acoustic word embeddings (AWEs) for the purpose of very low-resource keyword spotting. The ContrastiveTransfo…
Fine-Tuned Self-Supervised Speech Representations for Language Diarization in Multilingual Code-Switched Speech
Geoffrey Frost, Emily Morris, Joshua Jansen van Vüren +1
Annotating a multilingual code-switched corpus is a painstaking process requiring specialist linguistic expertise. This is partly due to the large number of language combinations t…
TB or not TB? Acoustic cough analysis for tuberculosis classification
Geoffrey Frost, Grant Theron, Thomas Niesler
In this work, we explore recurrent neural network architectures for tuberculosis (TB) cough classification. In contrast to previous unsuccessful attempts to implement deep architec…
Feature learning for efficient ASR-free keyword spotting in low-resource languages
Ewald van der Westhuizen, Herman Kamper, Raghav Menon +2
We consider feature learning for efficient keyword spotting that can be applied in severely under-resourced settings. The objective is to support humanitarian relief programmes by…
Multilingual training set selection for ASR in under-resourced Malian languages
Ewald van der Westhuizen, Trideba Padhi, Thomas Niesler
We present first speech recognition systems for the two severely under-resourced Malian languages Bambara and Maasina Fulfulde. These systems will be used by the United Nations as…
A Hybrid CNN-BiLSTM Voice Activity Detector
Nicholas Wilkinson, Thomas Niesler
This paper presents a new hybrid architecture for voice activity detection (VAD) incorporating both convolutional neural network (CNN) and bidirectional long short-term memory (BiL…
Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages
Trideba Padhi, Astik Biswas, Febe De Wet +2
In this work, we explore the benefits of using multilingual bottleneck features (mBNF) in acoustic modelling for the automatic speech recognition of code-switched (CS) speech in Af…
Semi-supervised acoustic modelling for five-lingual code-switched ASR using automatically-segmented soap opera speech
N. Wilkinson, A. Biswas, E. Yılmaz +3
This paper considers the impact of automatic segmentation on the fully-automatic, semi-supervised training of automatic speech recognition (ASR) systems for five-lingual code-switc…
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition
A. Biswas, F. de Wet, E. van der Westhuizen +1
We present an analysis of semi-supervised acoustic and language model training for English-isiZulu code-switched ASR using soap opera speech. Approximately 11 hours of untranscribe…
Semi-supervised Development of ASR Systems for Multilingual Code-switched Speech in Under-resourced Languages
Astik Biswas, Emre Yılmaz, Febe de Wet +2
This paper reports on the semi-supervised development of acoustic and language models for under-resourced, code-switched speech in five South African languages. Two approaches are…
Feature exploration for almost zero-resource ASR-free keyword spotting using a multilingual bottleneck extractor and correspondence autoencoders
Raghav Menon, Herman Kamper, Ewald van der Westhuizen +2
We compare features for dynamic time warping (DTW) when used to bootstrap keyword spotting (KWS) in an almost zero-resource setting. Such quickly-deployable systems aim to support…