Find out about the rapid advancement of speech and natural language in recent years.
Dr Julie Wall - Reader in Computer Science and Director of Impact and Innovation at the School of Architecture, Computing and Engineering in the University of East London
6:30pm - Webinar begins
7:45pm - Webinar ends
The rapid advancement of speech and natural language technology in recent years has been a significant aspect of the Industry 4.0 era. This progress has been propelled by GPU hardware and deep learning frameworks that leverage them, as well as the widespread adoption of open-source software by both the academic and commercial AI communities.
These developments have revolutionized the way humans interact with computers and have given rise to a multitude of commercial products that rely on speech recognition, natural language processing, and natural language understanding, collectively known as Conversational AI. In this context, machine learning and deep learning approaches play a vital role in enabling Conversational AI capabilities.
These techniques allow systems to learn from vast amounts of data, automatically extract relevant features, and make accurate predictions or generate human-like responses. They have led to significant breakthroughs in speech recognition, language translation, sentiment analysis, chatbots, and virtual assistants.
One practical application of Conversational AI can be found in the insurance domain, where speech and language technologies are harnessed to detect deception and combat fraud. By analysing the patterns, tone, and content of spoken or written interactions, machine learning models can identify suspicious behaviours or indications of fraudulent activities. This case study demonstrates the practical utility of speech and language technology in enhancing fraud detection and mitigation measures within the insurance industry.
About the speaker
Dr Julie Wall
Dr Julie Wall is a highly accomplished Reader in Computer Science and Director of Impact and Innovation at the School of Architecture, Computing and Engineering in the University of East London.
Her research focuses on designing intelligent systems to process and model temporal data, with a particular emphasis on speech and language applications. With extensive expertise in neural network architectures, Dr Wall has explored a range of models, from biologically inspired designs to computationally efficient machine and deep learning architectures.
She has applied these techniques to various data structures, including tabular, audio, images, video, and 3D feature data, achieving significant results.
Dr Wall has also developed production-grade deep learning and natural language understanding systems for a variety of platforms, including virtual and augmented reality environments. Her extensive research contributions have been widely recognized through the publication of over 40 high-quality journal and conference papers.
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