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We propose an unsupervised speech separation framework for mixtures of two unseen speakers in a single-channel setting based on deep neural networks (DNNs). We rely on a key assumption that two speakers could be well segregated if they are not too similar to each other. A dissimilarity measure between two speakers is first proposed to characterize the separation ability between competing speakers...
In this paper, we investigate high-resolution modeling units of deep neural networks (DNNs) from concrete to abstract for acoustic scene classification based on Gaussian mixture model (GMM) and ergodic hidden Markov model (HMM). A direct modeling strategy for DNN to classify acoustic scenes is to map each frame feature of an audio to one scene category. However, all frames tagged with the same label...
This paper proposes a novel segmentation-free approach using deep neural network based hidden Markov model (DNN-HMM) for offline handwritten Chinese text recognition. In the general Bayesian framework, three key issues are comprehensively investigated, namely feature extraction, character modeling, and language modeling. First, as for the feature extraction on the basis of each frame or sliding window,...
In this study, we propose a regression approach via deep neural network (DNN) for unsupervised speech separation in a single-channel setting. We rely on a key assumption that two speakers could be well segregated if they are not too similar to each other. A dissimilarity measure between two speakers is then proposed to characterize the separation ability between competing speakers. We demonstrate...
In this paper, a novel deep neural network (DNN) architecture is proposed to generate the speech features of both the target speaker and interferer for speech separation without using any prior information about the interfering speaker. DNN is adopted here to directly model the highly nonlinear relationship between speech features of the mixed signals and the two competing speakers. Experimental results...
This paper proposes a novel data-driven approach based on deep neural networks (DNNs) for single-channel speech separation. DNN is adopted to directly model the highly non-linear relationship of speech features between a target speaker and the mixed signals. Both supervised and semi-supervised scenarios are investigated. In the supervised mode, both identities of the target speaker and the interfering...
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