2 citations · 2 across the 5 of their papers we have counts for
5 papers
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation
Pu Wang, Hugo Van hamme
End-to-end transformer-based automatic speech recognition (ASR) systems often capture multiple speech traits in their learned representations that are highly entangled, leading to…
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…
A Study into Pre-training Strategies for Spoken Language Understanding on Dysarthric Speech
Pu Wang, Bagher BabaAli, Hugo Van hamme
End-to-end (E2E) spoken language understanding (SLU) systems avoid an intermediate textual representation by mapping speech directly into intents with slot values. This approach re…
Pre-training for low resource speech-to-intent applications
Pu Wang, Hugo Van hamme
Designing a speech-to-intent (S2I) agent which maps the users' spoken commands to the agents' desired task actions can be challenging due to the diverse grammatical and lexical pre…
A light transformer for speech-to-intent applications
Pu Wang, Hugo Van hamme
Spoken language understanding (SLU) systems can make life more agreeable, safer (e.g. in a car) or can increase the independence of physically challenged users. However, due to the…