wavlm

该论文对比了基于WavLM的音频分类器与基于GPT-4o零样本的转录文本分类器在母语识别任务上的表现，发现音频在低水平(A1)更优，而文本在A2至C1阶段配合完整转录时表现更好。

该论文提出一种噪声鲁棒的强制对齐方法，用上采样WLM特征替代MFCC的GMM-HMM对齐器，并显式建模填充词和非词汇语音事件，在Buckeye和FluencyBank上将词边界误差降低19%和17%，大幅减少灾难性错配。

This paper introduces CLeaD, a supervised contrastive alignment framework for cross-lingual depression detection from speech using WavLM embeddings. It reveals that previous results were inflated due to speaker identity leakage and achieves modest improvements on Mandarin speakers.