@inproceedings{WagnerBaumannEngertetal.2025, author = {Wagner, Dominik and Baumann, Ilja and Engert, Natalie and Lee, Seanie and N{\"o}th, Elmar and Riedhammer, Korbinian and Bocklet, Tobias}, title = {Personalized Fine-Tuning with Controllable Synthetic Speech from LLM-Generated Transcripts for Dysarthric Speech Recognition}, series = {Interspeech 2025}, booktitle = {Interspeech 2025}, publisher = {ISCA}, address = {ISCA}, issn = {2958-1796}, doi = {10.21437/Interspeech.2025-2155}, pages = {3294 -- 3298}, year = {2025}, abstract = {In this work, we present our submission to the Speech Accessibility Project challenge for dysarthric speech recognition. We integrate parameter-efficient fine-tuning with latent audio representations to improve an encoder-decoder ASR system. Synthetic training data is generated by fine-tuning Parler-TTS to mimic dysarthric speech, using LLM-generated prompts for corpus-consistent target transcripts. Personalization with x-vectors consistently reduces word error rates (WERs) over non-personalized fine-tuning. AdaLoRA adapters outperform full fine-tuning and standard low-rank adaptation, achieving relative WER reductions of ∼23\% and ∼22\%, respectively. Further improvements (∼5\% WER reduction) come from incorporating wav2vec 2.0-based audio representations. Training with synthetic dysarthric speech yields up to ∼7\% relative WER improvement over personalized fine-tuning alone.}, language = {en} }