TY - CHAP A1 - Baumann, Ilja A1 - Wagner, Dominik A1 - Riedhammer, Korbinian A1 - Bocklet, Tobias T1 - Optimized Self-supervised Training with BEST-RQ for Speech Recognition T2 - ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - Self-supervised learning has been successfully used for various speech related tasks, including automatic speech recognition. BERT-based Speech pre-Training with Random-projection Quantizer (BEST-RQ) has achieved state-of-the-art results in speech recognition. In this work, we further optimize the BEST-RQ approach using Kullback-Leibler divergence as an additional regularizing loss and multicodebook extension per cluster derived from low-level feature clustering. Preliminary experiments on train-100 split of LibriSpeech result in a relative improvement of 11.2% on test-clean by using multiple codebooks, utilizing a combination of cross-entropy and Kullback-Leibler divergence further reduces the word error rate by 4.5%. The proposed optimizations on full LibriSpeech pre-training and fine-tuning result in relative word error rate improvements of up to 23.8% on test-clean and 30.6% on testother using 6 codebooks. Furthermore, the proposed setup leads to faster convergence in pre-training and fine-tuning and additionally stabilizes the pre-training. KW - self-supervised learning KW - speech recognition KW - conformer KW - BEST-RQ Y1 - 2025 U6 - https://doi.org/10.1109/ICASSP49660.2025.10889362 SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Wagner, Dominik A1 - Baumann, Ilja A1 - Engert, Natalie A1 - Lee, Seanie A1 - Nöth, Elmar A1 - Riedhammer, Korbinian A1 - Bocklet, Tobias T1 - Personalized Fine-Tuning with Controllable Synthetic Speech from LLM-Generated Transcripts for Dysarthric Speech Recognition T2 - Interspeech 2025 N2 - 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. KW - speech recognition KW - dysarthric speech KW - personalization KW - low-rank adaptation KW - synthetic data Y1 - 2025 U6 - https://doi.org/10.21437/Interspeech.2025-2155 SN - 2958-1796 SP - 3294 EP - 3298 PB - ISCA CY - ISCA ER -