XANE背景声学嵌入：消融与聚类分析
XANE Background Acoustic Embeddings: Ablation and Clustering Analysis
AI总结：
本研究针对XANE可解释声学神经嵌入系统展开消融与聚类分析，验证了联合参数估计的积极作用，证明其在未见数据聚类任务上表现优于WavLM嵌入且与说话人嵌入互补。
摘要：
我们探究了近期提出的可解释声学神经嵌入（XANE）系统，该系统以非侵入式方式对语音信号的背景声学特征建模。XANE嵌入用于估计与信号背景声学属性相关的特定参数，这使得嵌入可通过这些参数进行解释。我们对XANE系统开展了消融研究，结果表明联合估计所有声学参数具有整体积极效果。此外，我们通过在未见过的测试数据上进行聚类实验，验证了XANE嵌入的价值，结果显示所提出的嵌入在三项不同任务上的平均F1得分为92%，显著优于基于WavLM的信号嵌入，且与说话人嵌入形成互补。
英文摘要：
We explore the recently proposed explainable acoustic neural embedding~(XANE) system that models the background acoustics of a speech signal in a non-intrusive manner. The XANE embeddings are used to estimate specific parameters related to the background acoustic properties of the signal which allows the embeddings to be explainable in terms of those parameters. We perform ablation studies on the XANE system and show that estimating all acoustic parameters jointly has an overall positive effect. Furthermore, we illustrate the value of XANE embeddings by performing clustering experiments on unseen test data and show that the proposed embeddings achieve a mean F1 score of 92\% for three different tasks, outperforming significantly the WavLM based signal embeddings and are complimentary to speaker embeddings.
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。
