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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

Authors

Md Iftekhar Tanveer , Diego Casabuena , Jussi Karlgren , Rosie Jones

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Learning Types > Unsupervised Learning Machine Learning > Learning Types > Transfer Learning Speech & Audio > Analysis > Speech Analysis

Keywords

unsupervised speaker diarization tuning free purity score language agnostic
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