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· 11 min read
Andre Mansikkaniemi

Speaker diarization often complements automatic speech recognition (ASR) by determining "Who spoke when?". One intriguing advancement in the field is the adoption of Self-Supervised Learning (SSL). By harnessing vast amounts of unlabelled audio data, SSL manages to improve multiple downstream tasks, including ASR and diarization, using the same pre-trained model. As we explore in this blog, the synergy between SSL and traditional methods not only boosts ASR accuracy but also aids in improving speaker diarization results.