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We propose a novel deep learning model, which supports permutation invariant training (PIT), for speaker independent multi-talker speech separation, commonly known as the cocktail-party problem. Different from most of the prior arts that treat speech separation as a multi-class regression problem and the deep clustering technique that considers it a(More)
—Despite the significant progress made in the recent years in dictating single-talker speech, the progress made in speaker independent multi-talker mixed speech separation and tracing, often referred to as the cocktail-party problem, has been less impressive. In this paper we propose a novel technique for attacking this problem. The core of our technique is(More)
In this paper, we study aspects of single microphone speech enhancement SE based on deep neural networks DNNs. Specifically, we explore the generalizability capabilities of state-of-the-art DNN-based SE systems with respect to the background noise type, the gender of the target speaker, and the signal-to-noise ratio SNR. Furthermore, we investigate how(More)
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