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arXiv · 2110.03380

Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity

You Jin Kim·Hee-Soo Heo·Jee-weon Jung·Youngki Kwon·Bong-Jin Lee·Joon Son Chung
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Abstract

The objective of this work is to train noise-robust speaker embeddings adapted for speaker diarisation. Speaker embeddings play a crucial role in the performance of diarisation systems, but they often capture spurious information such as noise, adversely affecting performance. Our previous work has proposed an auto-encoder-based dimensionality reduction module to help remove the redundant information. However, they do not explicitly separate such information and have also been found to be sensitive to hyper-parameter values. To this end, we propose two contributions to overcome these issues: (i) a novel dimensionality reduction framework that can disentangle spurious information from the speaker embeddings; (ii) the use of speech activity vector to prevent the speaker code from representing the background noise. Through a range of experiments conducted on four datasets, our approach consistently demonstrates the state-of-the-art performance among models without system fusion.

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You Jin Kim, Hee-Soo Heo, Jee-weon Jung, Youngki Kwon, Bong-Jin Lee, Joon Son Chung. 2021-10-07. Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity. https://arxiv.org/abs/2110.03380

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