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