21 citations · 154 across the 60 of their papers we have counts for
14 papers · 1 filter
Multitask Learning for Low Resource Spoken Language Understanding
Quentin Meeus, Marie-Francine Moens, Hugo Van hamme
We explore the benefits that multitask learning offer to speech processing as we train models on dual objectives with automatic speech recognition and intent classification or sent…
Bidirectional Representations for Low Resource Spoken Language Understanding
Quentin Meeus, Marie-Francine Moens, Hugo Van hamme
Most spoken language understanding systems use a pipeline approach composed of an automatic speech recognition interface and a natural language understanding module. This approach…
Impact of visual assistance for automated audio captioning
Wim Boes, Hugo Van hamme
We study the impact of visual assistance for automated audio captioning. Utilizing multi-encoder transformer architectures, which have previously been employed to introduce vision-…
Weak-Supervised Dysarthria-invariant Features for Spoken Language Understanding using an FHVAE and Adversarial Training
Jinzi Qi, Hugo Van hamme
The scarcity of training data and the large speaker variation in dysarthric speech lead to poor accuracy and poor speaker generalization of spoken language understanding systems fo…
Multi-Source Transformer Architectures for Audiovisual Scene Classification
Wim Boes, Hugo Van hamme
In this technical report, the systems we submitted for subtask 1B of the DCASE 2021 challenge, regarding audiovisual scene classification, are described in detail. They are essenti…
Optimizing Temporal Resolution Of Convolutional Recurrent Neural Networks For Sound Event Detection
Wim Boes, Hugo Van hamme
In this technical report, the systems we submitted for subtask 4 of the DCASE 2021 challenge, regarding sound event detection, are described in detail. These models are closely rel…
Learning to Jointly Transcribe and Subtitle for End-to-End Spontaneous Speech Recognition
Jakob Poncelet, Hugo Van hamme
TV subtitles are a rich source of transcriptions of many types of speech, ranging from read speech in news reports to conversational and spontaneous speech in talk shows and soaps.…
Weight Averaging: A Simple Yet Effective Method to Overcome Catastrophic Forgetting in Automatic Speech Recognition
Steven Vander Eeckt, Hugo Van hamme
Adapting a trained Automatic Speech Recognition (ASR) model to new tasks results in catastrophic forgetting of old tasks, limiting the model's ability to learn continually and to b…
Pre-trained Speech Representations as Feature Extractors for Speech Quality Assessment in Online Conferencing Applications
Bastiaan Tamm, Helena Balabin, Rik Vandenberghe +1
Speech quality in online conferencing applications is typically assessed through human judgements in the form of the mean opinion score (MOS) metric. Since such a labor-intensive a…
Impact of temporal resolution on convolutional recurrent networks for audio tagging and sound event detection
Wim Boes, Hugo Van hamme
Many state-of-the-art systems for audio tagging and sound event detection employ convolutional recurrent neural architectures. Typically, they are trained in a mean teacher setting…
Relating the fundamental frequency of speech with EEG using a dilated convolutional network
Corentin Puffay, Jana Van Canneyt, Jonas Vanthornhout +2
To investigate how speech is processed in the brain, we can model the relation between features of a natural speech signal and the corresponding recorded electroencephalogram (EEG)…
Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders
Lies Bollens, Tom Francart, Hugo Van Hamme
The electroencephalogram (EEG) is a powerful method to understand how the brain processes speech. Linear models have recently been replaced for this purpose with deep neural networ…
Bottleneck Low-rank Transformers for Low-resource Spoken Language Understanding
Pu Wang, Hugo Van hamme
End-to-end spoken language understanding (SLU) systems benefit from pretraining on large corpora, followed by fine-tuning on application-specific data. The resulting models are too…
Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech Recognition
Steven Vander Eeckt, Hugo Van hamme
Learning a set of tasks in sequence remains a challenge for artificial neural networks, which, in such scenarios, tend to suffer from Catastrophic Forgetting (CF). The same applies…