4 papers
Joint Training of Speaker Embedding Extractor, Speech and Overlap Detection for Diarization
Petr Pálka, Federico Landini, Dominik Klement +4
In spite of the popularity of end-to-end diarization systems nowadays, modular systems comprised of voice activity detection (VAD), speaker embedding extraction plus clustering, an…
Challenging margin-based speaker embedding extractors by using the variational information bottleneck
Themos Stafylakis, Anna Silnova, Johan Rohdin +2
Speaker embedding extractors are typically trained using a classification loss over the training speakers. During the last few years, the standard softmax/cross-entropy loss has be…
Discriminative Training of VBx Diarization
Dominik Klement, Mireia Diez, Federico Landini +4
Bayesian HMM clustering of x-vector sequences (VBx) has become a widely adopted diarization baseline model in publications and challenges. It uses an HMM to model speaker turns, a…
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Marc Delcroix, Naohiro Tawara, Mireia Diez +6
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods. EEND-VC esti…