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The emergence of virtual personal assistants such as SIRI, Cortana, Echo, and Google Now, is generating increasing interest in research in speech understanding and spoken interaction. However, whilst the ability of these agents to recognize conversational speech is maturing rapidly, their ability to understand and interact is still limited to a few specific domains, such as weather information, local businesses, and some simple chit-chat. Their conversational capabilities are not necessarily apparent to users. Interaction typically depends on handcrafted scripts and is often guided by simple commands. Deployed dialogue models do not fully make use of the large amount of data that these agents generate. Promising approaches that involve statistical models, big data analysis, representation of knowledge (hierarchical, relations, etc.), utilizing and enriching semantic graphs with natural language components, multi-modality, etc. are being explored in multiple communities, such as natural language processing (NLP), speech processing, machine learning (ML), and information retrieval. However, we are still only scratching the surface in this field. The goal of the special issue is to bring together both applied and theoretical studies in spoken/natural language processing and machine learning to facilitate the emergence of new frameworks that can help advance modern conversational systems.
The special issue is a follow up of the NIPS 2015 Workshop on Spken Language Understanding and Interaction, but is not limited to those papers. All new and original submissions are encouraged. Papers will be peer-reviewed according to the journal standards.
Additional information can be found here.
Submissions due by 10 April 2016
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