1 citations · 3 across the 20 of their papers we have counts for
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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…
Less can be More: What Aspects of Speech Drive End-of-Turn Detection
Rini Sharon, Manickavela A, Kadri Hacioglu +1
In conversational AI, detecting when a speaker has finished talking is crucial for natural turn taking. While recent work incorporates semantics, the relative contribution of diffe…
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…
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…