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From Generative to Discriminative: Lightweight Adaptation of SpeechLLMs for Multimodal Emotion Recognition
Hasindri Watawana, Sergio Burdisso, Esaú Villatoro-Tello +4
SpeechLLMs can recognise emotion by generating a label token, but this approach relies on the LLM's existing output representations. We compare this generative approach with a disc…
How to Leverage Synthetic Speech for LLM-Based ASR Systems?
Yanis Labrak, Dairazalia Sanchez-Cortes, Sergio Burdisso +9
In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is a…
Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR
Thibault Bañeras-Roux, Sergio Burdisso, Esaú Villatoro-Tello +9
Conventional end-to-end automatic speech recognition (ASR) systems rely on paired speech-text data for domain adaptation. Recent LLM-based ASR architectures connect a speech encode…
Reducing Prompt Sensitivity in LLM-based Speech Recognition Through Learnable Projection
Sergio Burdisso, Esaú Villatoro-Tello, Shashi Kumar +7
LLM-based automatic speech recognition (ASR), a well-established approach, connects speech foundation models to large language models (LLMs) through a speech-to-LLM projector, yiel…
Text-only adaptation in LLM-based ASR through text denoising
Andrés Carofilis, Sergio Burdisso, Esaú Villatoro-Tello +8
Adapting large language model (LLM)-based automatic speech recognition (ASR) systems to new domains using text-only data is a significant yet underexplored challenge. Standard fine…