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Jimmy Tobin

5 accepted papers

2025

Speech Recognition with LLMs Adapted to Disordered Speech Using Reinforcement Learning

ICASSP 2025accepted

We introduce a large language model (LLM) capable of processing speech inputs and show that tuning it further with reinforcement learning on human preference (RLHF) enables it to adapt better to disordered speech than traditional fine-tuning. Our method replaces low-frequency text tokens in an LLM’s…

Cited by 0SourceScholar
2025

Towards a Single ASR Model That Generalizes to Disordered Speech

ICASSP 2025accepted

This study investigates the impact of integrating a dataset of disordered speech recordings (~1,000 hours) into the fine-tuning of a near state-of-the-art ASR baseline system. Contrary to what one might expect, despite the data being less than 1% of the training data of the ASR system, we find a con…

Cited by 0SourceScholar
2024

Large Language Models As A Proxy For Human Evaluation In Assessing The Comprehensibility Of Disordered Speech Transcription

ICASSP 2024accepted

Automatic Speech Recognition (ASR) systems, despite significant advances in recent years, still have much room for improvement particularly in the recognition of disordered speech. Even so, erroneous transcripts from ASR models can help people with disordered speech be better understood, especially…

Cited by 0SourceScholar
2023

Speech Intelligibility Classifiers from 550k Disordered Speech Samples

ICASSP 2023accepted

We developed dysarthric speech intelligibility classifiers on 551,176 disordered speech samples contributed by a diverse set of 468 speakers, with a range of self-reported speaking disorders and rated for their overall intelligibility on a five-point scale. We trained three models following differen…

Cited by 0SourceScholar