1 citations · 3 across the 20 of their papers we have counts for
8 papers · 2 filters
MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents
Arkajyoti Chakraborty, Adam Zernik, Ishika Kulkarni +6
Task-oriented conversational agents remain fragile under realistic conditions, especially when users exhibit non-cooperative behavior. Existing function-calling benchmarks underrep…
ToolRACER: A Robust Agentic Conversation Emulation Resource for Agent Training and Evaluation
Arkajyoti Chakraborty, Aryan Tayal, Ishika Agarwal +5
Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when users exhibit non-cooperative…
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…
LLMs Get Smarter from Targeted Synthetic Multilingual Data
Ishika Agarwal, Arkajyoti Charaborty, Tanner Sorensen +2
Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt. In other words, a language model output…
When Synthetic Speech Is All You Have: Better Call GRPO
Shashi Kumar, Yanis Labrak, Hasindri Watawana +5
LLM-based ASR adapted to regulated domains such as banking is bottlenecked by privacy: real speech is costly and legally constrained to collect, making synthetic text-to-speech (TT…
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…
Abstract Compression of Conversational Audio Context for LLM-based ASR
Shashi Kumar, Esaú Villatoro-Tello, Sergio Burdisso +7
Standard LLM-based speech recognition systems process utterances in isolation, limiting their ability to leverage conversational context. Here we study whether multimodal context f…