用于SLT 2024低资源构音障碍唤醒词检测挑战赛的PB-LRDWWS系统
PB-LRDWWS System for the SLT 2024 Low-Resource Dysarthria Wake-Up Word Spotting Challenge
AI总结：
针对SLT 2024低资源构音障碍唤醒词检测挑战赛，提出结合微调HuBERT特征提取器（三阶段交叉熵微调）与原型分类（余弦相似度匹配）的PB-LRDWWS系统，在Test-B获第二名。
摘要：
针对SLT 2024低资源构音障碍唤醒词检测（LRDWWS）挑战赛，我们提出PB-LRDWWS系统。该系统结合构音障碍语音内容特征提取器（用于原型构建）与基于原型的分类方法。特征提取器是通过三阶段微调过程（使用交叉熵损失）得到的微调HuBERT模型，该模型从目标构音障碍说话人的注册语音中提取特征以构建原型；分类通过计算目标构音障碍说话人评估语音的HuBERT特征与原型间的余弦相似度实现。尽管方法简单，实验结果表明其有效，该系统在LRDWWS挑战赛最终Test-B中获第二名。
英文摘要：
For the SLT 2024 Low-Resource Dysarthria Wake-Up Word Spotting (LRDWWS) Challenge, we introduce the PB-LRDWWS system. This system combines a dysarthric speech content feature extractor for prototype construction with a prototype-based classification method. The feature extractor is a fine-tuned HuBERT model obtained through a three-stage fine-tuning process using cross-entropy loss. This fine-tuned HuBERT extracts features from the target dysarthric speaker's enrollment speech to build prototypes. Classification is achieved by calculating the cosine similarity between the HuBERT features of the target dysarthric speaker's evaluation speech and prototypes. Despite its simplicity, our method demonstrates effectiveness through experimental results. Our system achieves second place in the final Test-B of the LRDWWS Challenge.
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。
