Semi-supervised Time Domain Target Speaker Extraction with Attention

Zhepei Wang; Ritwik Giri; Shrikant Venkataramani; Umut Isik; Jean-Marc Valin; Paris Smaragdis; Mike Goodwin; Arvindh Krishnaswamy

arXiv · 2206.09072

Semi-supervised Time Domain Target Speaker Extraction with Attention

Zhepei Wang·Ritwik Giri·Shrikant Venkataramani·Umut Isik·Jean-Marc Valin·Paris Smaragdis·Mike Goodwin·Arvindh Krishnaswamy

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Abstract

In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It consists of a pre-trained speaker embedder network and a separator network based on transformer encoder blocks. We study multiple methods to combine speaker information with the input mixture, and the resulting Exformer architecture obtains superior extraction performance compared to prior time-domain networks. Furthermore, we investigate a two-stage procedure to train the model using mixtures without reference signals upon a pre-trained supervised model. Experimental results show that the proposed semi-supervised learning procedure improves the performance of the supervised baselines.

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Zhepei Wang, Ritwik Giri, Shrikant Venkataramani, Umut Isik, Jean-Marc Valin, Paris Smaragdis, Mike Goodwin, Arvindh Krishnaswamy. 2022-06-18. Semi-supervised Time Domain Target Speaker Extraction with Attention. https://arxiv.org/abs/2206.09072

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