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VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking

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arxiv 1810.04826 v6 pith:NRIAZH7J submitted 2018-10-11 eess.AS cs.LGeess.SPstat.ML

VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking

Quan Wang , Hannah Muckenhirn , Kevin Wilson , Prashant Sridhar , Zelin Wu , John Hershey , Rif A. Saurous , Ron J. Weiss
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Ye Jia Ignacio Lopez Moreno
This is my paper
classification eess.AS cs.LGeess.SPstat.ML
keywords speakersignalsspectrogrammaskingmulti-speakernetworkproducesrecognition
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We achieve this by training two separate neural networks: (1) A speaker recognition network that produces speaker-discriminative embeddings; (2) A spectrogram masking network that takes both noisy spectrogram and speaker embedding as input, and produces a mask. Our system significantly reduces the speech recognition WER on multi-speaker signals, with minimal WER degradation on single-speaker signals.

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