Unsupervised Speaker Diarization that is Agnostic to Language Overlap Aware and Free of Tuning


Podcasts are conversational in nature and speaker changes are frequent—requiring speaker diarization for content understanding. We propose an unsupervised technique for speaker diarization without relying on language-specific components. The algorithm is overlap-aware and does not require information about the number of speakers. Our approach shows 79% improvement on purity scores (34% on F-score) against the Google Cloud Platform solution on podcast data.


June 2023 | ICASSP

Contrastive Learning-based Audio to Lyrics Alignment for Multiple Languages

Simon Durand, Daniel Stoller, Sebastian Ewert

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Improving Content Retrievability in Search with Controllable Query Generation

Gustavo Penha, Enrico Palumbo, Maryam Aziz, Alice Wang, and Hugues Bouchard

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Non-parametric identifiability and sensitivity analysis of synthetic control models

Jakob Zeitler, Athanasios Vlontzos, Ciarán Mark Gilligan-Lee