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This paper introduces an improved target speaker extractor, referred to asSpeakerfilter-Pro, based on our previous Speakerfilter model. The Speakerfilteruses a bi-direction gated recurrent unit (BGRU) module to characterize thetarget speaker from anchor speech and use a convolutional recurrent network(CRN) module to separate the target speech from a noisy signal.Different fromthe Speakerfilter, the Speakerfilter-Pro sticks a WaveUNet module in thebeginning and the ending, respectively. The WaveUNet has been proven to have abetter ability to perform speech separation in the time domain. In order toextract the target speaker information better, the complex spectrum instead ofthe magnitude spectrum is utilized as the input feature for the CRN module.Experiments are conducted on the two-speaker dataset (WSJ0-mix2) which iswidely used for speaker extraction. The systematic evaluation shows that theSpeakerfilter-Pro outperforms the Speakerfilter and other baselines, andachieves a signal-to-distortion ratio (SDR) of 14.95 dB.

Speakerfilter-Pro: an improved target speaker extractor combines the time domain and frequency domain