4 citations · 5 across the 2 of their papers we have counts for
4 papers · 1 filter
Location-based training for multi-channel talker-independent speaker separation
Hassan Taherian, Ke Tan, DeLiang Wang
Permutation-invariant training (PIT) is a dominant approach for addressing the permutation ambiguity problem in talker-independent speaker separation. Leveraging spatial informatio…
SAGRNN: Self-Attentive Gated RNN for Binaural Speaker Separation with Interaural Cue Preservation
Ke Tan, Buye Xu, Anurag Kumar +2
Most existing deep learning based binaural speaker separation systems focus on producing a monaural estimate for each of the target speakers, and thus do not preserve the interaura…
Audio-Visual Speech Separation and Dereverberation with a Two-Stage Multimodal Network
Ke Tan, Yong Xu, Shi-Xiong Zhang +2
Background noise, interfering speech and room reverberation frequently distort target speech in real listening environments. In this study, we address joint speech separation and d…
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling
Peidong Wang, Ke Tan, DeLiang Wang
Monaural speech enhancement has made dramatic advances since the introduction of deep learning a few years ago. Although enhanced speech has been demonstrated to have better intell…