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arXiv · 2103.13443

Blind Speech Separation and Dereverberation using Neural Beamforming

Lukas Pfeifenberger·Franz Pernkopf
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Abstract

In this paper, we present the Blind Speech Separation and Dereverberation (BSSD) network, which performs simultaneous speaker separation, dereverberation and speaker identification in a single neural network. Speaker separation is guided by a set of predefined spatial cues. Dereverberation is performed by using neural beamforming, and speaker identification is aided by embedding vectors and triplet mining. We introduce a frequency-domain model which uses complex-valued neural networks, and a time-domain variant which performs beamforming in latent space. Further, we propose a block-online mode to process longer audio recordings, as they occur in meeting scenarios. We evaluate our system in terms of Scale Independent Signal to Distortion Ratio (SI-SDR), Word Error Rate (WER) and Equal Error Rate (EER).

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BibTeXRIS

Lukas Pfeifenberger, Franz Pernkopf. 2021-11-05. Blind Speech Separation and Dereverberation using Neural Beamforming. https://arxiv.org/abs/2103.13443

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