Assessing the Robustness of Spectral Clustering for Deep Speaker
  Diarization
Nikhil Raghav Md Sahidullah
Clustering speaker embeddings is crucial in speaker diarization but hasn't
received as much focus as other components. Moreover, the robustness of speaker
diarization across various datasets hasn't been explored when the development
and evaluation data are from different domains. To bridge this gap, this study
thoroughly examines spectral clustering for both same-domain and cross-domain
speaker diarization. Our extensive experiments on two widely used corpora, AMI
and DIHARD, reveal the performance trend of speaker diarization in the presence
of domain mismatch. We observe that the performance difference between two
different domain conditions can be attributed to the role of spectral
clustering. In particular, keeping other modules unchanged, we show that
differences in optimal tuning parameters as well as speaker count estimation
originates due to the mismatch. This study opens several future directions for
speaker diarization research.