  As a practical alternative of speech separation, target speaker extraction
(TSE) aims to extract the speech from the desired speaker using additional
speaker cue extracted from the speaker. Its main challenge lies in how to
properly extract and leverage the speaker cue to benefit the extracted speech
quality. The cue extraction method adopted in majority existing TSE studies is
to directly utilize discriminative speaker embedding, which is extracted from
the pre-trained models for speaker verification. Although the high speaker
discriminability is a most desirable property for speaker verification task, we
argue that it may be too sophisticated for TSE. In this study, we propose that
a simplified speaker cue with clear class separability might be preferred for
TSE. To verify our proposal, we introduce several forms of speaker cues,
including naive speaker embedding (such as, x-vector and xi-vector) and new
speaker embeddings produced from sparse LDA-transform. Corresponding TSE models
are built by integrating these speaker cues with SepFormer (one SOTA speech
separation model). Performances of these TSE models are examined on the
benchmark WSJ0-2mix dataset. Experimental results validate the effectiveness
and generalizability of our proposal, showing up to 9.9% relative improvement
in SI-SDRi. Moreover, with SI-SDRi of 19.4 dB and PESQ of 3.78, our best TSE
system significantly outperforms the current SOTA systems and offers the top
TSE results reported till date on the WSJ0-2mix.
