标题： 将ASR与LLMs结合用于构音障碍语音识别：自监督和生成方法的基准测试 显示英文标题

标题： Bridging ASR and LLMs for Dysarthric Speech Recognition: Benchmarking Self-Supervised and Generative Approaches

摘要： 语音识别（ASR）由于发音体的失真和高变异性而面临挑战。 虽然自监督的ASR模型如Wav2Vec、HuBERT和Whisper表现出潜力，但它们在发音障碍语音中的有效性仍不明确。 本研究系统地评估了这些模型与不同的解码策略，包括CTC、seq2seq和LLM增强解码（BART、GPT-2、Vicuna）。 我们的贡献包括（1）对发音障碍语音的ASR架构进行基准测试，（2）引入基于LLM的解码以提高可理解性，（3）分析跨数据集的泛化能力，以及（4）提供不同严重程度下的识别错误见解。 研究结果表明，LLM增强的解码通过利用语言约束进行发音恢复和语法修正，提高了发音障碍语音的ASR性能。 显示英文摘要

摘要： Speech Recognition (ASR) due to phoneme distortions and high variability. While self-supervised ASR models like Wav2Vec, HuBERT, and Whisper have shown promise, their effectiveness in dysarthric speech remains unclear. This study systematically benchmarks these models with different decoding strategies, including CTC, seq2seq, and LLM-enhanced decoding (BART,GPT-2, Vicuna). Our contributions include (1) benchmarking ASR architectures for dysarthric speech, (2) introducing LLM-based decoding to improve intelligibility, (3) analyzing generalization across datasets, and (4) providing insights into recognition errors across severity levels. Findings highlight that LLM-enhanced decoding improves dysarthric ASR by leveraging linguistic constraints for phoneme restoration and grammatical correction.