面向帕金森病患者的自动语音识别微调：提升语音技术可及性的有效策略
Fine-Tuning Automatic Speech Recognition for People with Parkinson's: An Effective Strategy for Enhancing Speech Technology Accessibility
AI总结：
本文针对帕金森病患者语音识别难题，提出在SAP数据集上微调ASR模型，并采用多任务学习联合估计损伤严重程度，使词错误率较LibriSpeech基线显著降低。
摘要：
本文通过在语音可及性项目（SAP）2023-10-05数据包上微调预训练自动语音识别（ASR）模型来增强构音障碍和发声障碍语音识别，该数据包包含253名帕金森病患者的语音。实验测试了对脑性瘫痪有效的方法，包括使用说话人聚类和严重程度依赖模型、加权微调和多任务学习。最佳结果使用多任务学习模型获得，其中ASR被训练以产生说话人损伤严重程度的估计作为辅助输出。与仅使用LibriSpeech数据微调的基线模型相比，所得词错误率显著改善，与分别在100小时和960小时LibriSpeech数据上微调相比，词错误率改善了37.62%和26.97%。
英文摘要：
This paper enhances dysarthric and dysphonic speech recognition by fine-tuning pretrained automatic speech recognition (ASR) models on the 2023-10-05 data package of the Speech Accessibility Project (SAP), which contains the speech of 253 people with Parkinson's disease. Experiments tested methods that have been effective for Cerebral Palsy, including the use of speaker clustering and severity-dependent models, weighted fine-tuning, and multi-task learning. Best results were obtained using a multi-task learning model, in which the ASR is trained to produce an estimate of the speaker's impairment severity as an auxiliary output. The resulting word error rates are considerably improved relative to a baseline model fine-tuned using only Librispeech data, with word error rate improvements of 37.62\% and 26.97\% compared to fine-tuning on 100h and 960h of LibriSpeech data, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
