14 papers · 1 filter
Generative vs. Encoder Large Language Models for ASR Evaluation: A Comparative Study
Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil +6
Automatic Speech Recognition (ASR) is typically evaluated using Word Error Rate (WER), which poorly reflects semantic similarity. While embedding-based metrics correlate better wit…
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…
Geometric Latent Reasoning Induces Shorter Generations in LLMs
Shashi Kumar, Yacouba Kaloga, Petr Motlicek +2
Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive, length-sensitive, and const…
Evaluation of Automatic Speech Recognition Using Generative Large Language Models
Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil +6
Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based semantic metrics are better corr…
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…