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Enhancing End-to-End Multi-Channel Speech Separation Via Spatial Feature Learning.

, , , , , , , and . ICASSP, page 7319-7323. IEEE, (2020)

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Multi-band PIT and Model Integration for Improved Multi-channel Speech Separation., , , and . ICASSP, page 705-709. IEEE, (2019)Deep Neural Networks for Single-Channel Multi-Talker Speech Recognition., , , and . IEEE ACM Trans. Audio Speech Lang. Process., 23 (10): 1670-1679 (2015)Maximum Entropy Based Generic Filter for Language Model Adaptation., , , and . ICASSP (1), page 597-600. IEEE, (2005)Speech recognition with prediction-adaptation-correction recurrent neural networks., , , and . ICASSP, page 5004-5008. IEEE, (2015)Large-Margin Minimum Classification Error Training for Large-Scale Speech Recognition Tasks., , , and . ICASSP (4), page 1137-1140. IEEE, (2007)Integrated adaptation with multi-factor joint-learning for far-field speech recognition., , , and . ICASSP, page 5770-5774. IEEE, (2016)Scalable stacking and learning for building deep architectures., , and . ICASSP, page 2133-2136. IEEE, (2012)Neural Network Based Multi-Factor Aware Joint Training for Robust Speech Recognition., , and . IEEE ACM Trans. Audio Speech Lang. Process., 24 (12): 2231-2240 (2016)Alleviate Cross-chunk Permutation through Chunk-level Speaker Embedding for Blind Speech Separation., , , and . APSIPA, page 325-331. IEEE, (2019)Joint separation and denoising of noisy multi-talker speech using recurrent neural networks and permutation invariant training., , , and . MLSP, page 1-6. IEEE, (2017)