2022
INTERSPEECH
INTERSPEECH 2022
Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free
Abstract
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.
🌉
Interdisciplinary Bridge
- Machine Learning and Speech & Audio
🧭
Keyword Pioneer
- unsupervised speaker diarization
🐝
Cross-Pollinator
- Artificial Intelligence, Deep Learning, Machine Learning, Natural Language Processing, Speech & Audio