  Target speaker extraction aims to isolate a specific speaker's voice from a
composite of multiple sound sources, guided by an enrollment utterance or
called anchor. Current methods predominantly derive speaker embeddings from the
anchor and integrate them into the separation network to separate the voice of
the target speaker. However, the representation of the speaker embedding is too
simplistic, often being merely a 1*1024 vector. This dense information makes it
difficult for the separation network to harness effectively. To address this
limitation, we introduce a pioneering methodology called Hierarchical
Representation (HR) that seamlessly fuses anchor data across granular and
overarching 5 layers of the separation network, enhancing the precision of
target extraction. HR amplifies the efficacy of anchors to improve target
speaker isolation. On the Libri-2talker dataset, HR substantially outperforms
state-of-the-art time-frequency domain techniques. Further demonstrating HR's
capabilities, we achieved first place in the prestigious ICASSP 2023 Deep Noise
Suppression Challenge. The proposed HR methodology shows great promise for
advancing target speaker extraction through enhanced anchor utilization.
