无训练融合神经与嵌入的说话人分离
Training-Free Affinity Fusion of Neural and Embedding-Based Speaker Diarization
TFAF方法无需额外训练就能融合两种说话人分离技术，在AMI和CALLHOME基准上表现更好。
研究人员提出训练自由亲和力融合(TFAF)方法，将神经说话人分离推断的说话人结构整合到基于嵌入的分离系统中。该方法在AMI和CALLHOME数据集上实现了比两种独立系统更低的错误率(DER)。在AMI数据集上，融合还改进了说话人属性转录效果。消融实验显示神经说话人分区贡献了大部分增益。
Training-Free Affinity Fusion of Neural and Embedding-Based Speaker Diarization
Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermediate representations are not directly compatible. We introduce Training-Free Affinity Fusion (TFAF), which integrates the speaker structure inferred by a neural diarizer into an embedding-based diarization system. The neural speaker partition is used to condition local speaker representations, from which we construct a continuous affinity matrix and combine it with the embedding-based acoustic affinity before a single global clustering step. The method requires no additional training, shared embedding space, speaker-label alignment, or hard transfer of the neural diarizer's speaker count. Experiments on AMI and CALLHOME show consistent DER improvements over both constituent systems; on AMI, fusion also improves speaker-attributed transcription. Ablations show that the neural speaker partition accounts for most of the gain, while retaining the continuous embedding-based affinities provides additional benefit over hard partition fusion.
