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Sparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk, we start by presenting this model and its features, and then turn to describe two special cases from it: 1) the convolutional sparse coding (CSC) and 2) its multi-layered version (ML-CSC). Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to deep-learning architectures.

Thanks to the introduction of deep networks for representation learning, the past few years have seen a dramatic increase in the performance of recognition systems. However, the mathematical reasons for this success remain elusive. For example, a key issue is that the neural network training problem is nonconvex, hence, optimization algorithms may not return a global minima. In addition, the regularization properties of algorithms such as dropout remain poorly understood.

Plenary talk delivered at ICIP 2019 in Taipei.

A person’s speech is strongly conditioned by his own articulators and the language(s) he speaks, hence, rendering speech in an interspeaker or interlanguage manner from a source speaker’s speech data collected in his native language that is both academically challenging and technology/application desirable. The quality of the rendered speech is assessed in three dimensions: naturalness, intelligibility, and similarity to the source speaker. Usually, the three criteria cannot be all met when rendering is done in both cross-speaker and cross-language ways.

Machine learning and, in particular, neural networks, have made great advances in the last few years for products that are used by millions of people, most notably in speech recognition, image recognition, and recently in neural machine translation. Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference.

Farfield speech recognition has become a popular research area in the past few years, from more research focused activities such as the CHiME Challenges, to the launches of Amazon Echo and Google Home. This talk will describe the research efforts around Google Home. Most multichannel ASR systems commonly separate speech enhancement, including localization, beamforming, and postfiltering from acoustic modeling. In this talk, we will introduce a framework to do multichannel enhancement jointly with acoustic modeling using deep neural networks.

Human performance is often appearing as a glass ceiling when it comes to automatic speech and speaker analysis. In some tasks, such as health monitoring, however, automatic analysis has successfully started to break this ceiling. The field has benefited from more than a decade of deep neural learning approaches such as recurrent LSTM nets and deep RBMs by now; however, recently, a further major boost could be witnessed.

Computers have been changing the lives of the blind people. Voice synthesis technology has improved their educational environment and job opportunities by allowing them to access online services. Now, the new AI technologies are reaching the point where computers can help in sensing, recognizing, and understanding our living world, real-world. I will first introduce the concept of cognitive assistant for the blind, which will help blind and visually impaired to explore surroundings and enjoy city environment by assisting their missing visual sense by the power of integrated AI technologies.