58 citations · 68 across the 8 of their papers we have counts for
10 papers
WHISMA: A Speech-LLM to Perform Zero-shot Spoken Language Understanding
Mohan Li, Cong-Thanh Do, Simon Keizer +3
Speech large language models (speech-LLMs) integrate speech and text-based foundation models to provide a unified framework for handling a wide range of downstream tasks. In this p…
Prompting Whisper for QA-driven Zero-shot End-to-end Spoken Language Understanding
Mohan Li, Simon Keizer, Rama Doddipatla
Zero-shot spoken language understanding (SLU) enables systems to comprehend user utterances in new domains without prior exposure to training data. Recent studies often rely on lar…
Evaluating Large Language Models for Document-grounded Response Generation in Information-Seeking Dialogues
Norbert Braunschweiler, Rama Doddipatla, Simon Keizer +1
In this paper, we investigate the use of large language models (LLMs) like ChatGPT for document-grounded response generation in the context of information-seeking dialogues. For ev…
Adversarial learning of neural user simulators for dialogue policy optimisation
Simon Keizer, Caroline Dockes, Norbert Braunschweiler +2
Reinforcement learning based dialogue policies are typically trained in interaction with a user simulator. To obtain an effective and robust policy, this simulator should generate…
Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling
Simon Keizer, Norbert Braunschweiler, Svetlana Stoyanchev +1
A major bottleneck for building statistical spoken dialogue systems for new domains and applications is the need for large amounts of training data. To address this problem, we ado…
A study on cross-corpus speech emotion recognition and data augmentation
Norbert Braunschweiler, Rama Doddipatla, Simon Keizer +1
Models that can handle a wide range of speakers and acoustic conditions are essential in speech emotion recognition (SER). Often, these models tend to show mixed results when prese…