Large-Scale Discriminative Modelling for Data-Intensive Speech and Language Processing

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As the wireless Internet and smartphones have become more and more prevalent in our daily life, many service providers have used the so-called cloud-computing framework to deliver various types of speech and language related services to customers. Under this background, it is very easy and fast for these providers to accumulate massive amount of real-world data in their central servers. Therefore, it has become a very interesting research topic regarding how to take advantage of the mass data available in this unprecedented big data era to further boost up performance to next level for many real world applications. In this research program, we will study this problem in the context of data-intensive speech and language applications, such as automatic speech recognition, text categorization and spoken language processing. We will first focus our research on investigating new discriminative models that are effective to model the most important and pertinent information available in the massive training data, which may be noisy and unlabelled. Meanwhile, we will also design efficient learning algorithms that are flexible enough to take full advantage of currently popular parallel computing facilities, such as multi-core CPUs, GPUs and map-reduce based distributed computing platform. More specifically, based on our previous research on margin-based discriminative learning, we will study three important sub-topics in this research program: i) how to extract discriminative and compact features for speech and language data; ii) how to build flexible discriminative models capable of dealing with a vast quantity of data in data-intensive speech and language applications; iii) how to parallelize learning algorithms to efficiently solve large-scale optimization in model estimation. We believe these issues are critical for us to fully benefit from massive real-world data in many data-intensive speech and language applications. This research program will help to advance large-scale learning and automatic knowledge discovery technologies for many Canadian and International industrial IT sections that are based on the more and more popular cloud-computing model.