Deep neural networks (DNNs) are often used to tackle the single channel source separation (SCSS) problem by predicting time-frequency masks. The predicted masks are then used to separate the sources from the mixed signal. Different types of masks produce separated sources with different levels of distortion and interference. Some types of masks produce separated sources with low distortion, whi...

In this paper, we propose a method that use i-vectors and model adaptation techniques to improve the performance of deep neural networks(DNNs) based multi-accent Mandarin speech recognition. I-vectors which are speaker-specific features have been proved to be effective when used in accent identification. They can be used in company with conventional spectral features as the input features of DN...

Deep Neural Networks (DNNs) have been shown to outperform traditional Gaussian Mixture Models in many Automatic Speech Recognition tasks. In this work, we investigate the potential of modeling long temporal acoustic contexts using DNNs. The complete temporal context is split into several subcontexts. Multiple sub-context DNNs initialized with the same set of Restricted Boltzmann Machines are fi...

Despite their great success, there is still no comprehensive theoretical understanding of learning with Deep Neural Networks (DNNs) or their inner organization. Previous work [Tishby and Zaslavsky (2015)] proposed to analyze DNNs in the Information Plane; i.e., the plane of the Mutual Information values that each layer preserves on the input and output variables. They suggested that the goal of...

We present the work done by our group for the 2015 language recognition evaluation (LRE) organized by the National Institute of Standards and Technology (NIST). The focus of this evaluation was the development of language recognition systems for clusters of closely related languages using training data released by NIST. This training data contained a highly imbalanced sample from the languages ...

With pervasive applications of medical imaging in health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. Since manual annotation suffers limited reproducibility, arduous efforts, and excessive time, automatic segmentation is desired to process increasingly larger scale histopathological data. Recently, deep neural ...

We seek to (i) characterize the learning architectures exploited in biological neural networks for training on very few samples, and (ii) port these algorithmic structures to a machine learning context. The Moth Olfactory Network is among the simplest biological neural systems that can learn, and its architecture includes key structural elements widespread in biological neural nets, such as cas...