Spoken language understanding and interaction: machine learning for human-like conversational systems. (November 2017)

• Record Type: Journal Article
• Authors: Gašić, Milica; Hakkani-Tür, Dilek; Celikyilmaz, Asli
• Is Part Of: Computer speech & language. Volume 46(2017)
• Page Start: 249
• Page End: 251
• Publication Date: 2017-11
• Subjects: Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.05.006
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: In recent years, the interest in research in speech understanding and spoken interaction has soared due to the emergence of virtual personal assistants. However, while the ability of these agents to recognise conversational speech is maturing rapidly, their ability to understand and interact is still limited. At the same time we have witnessed the development of the number of models based on machine learning that made a huge impact on spoken language understanding accuracies and the interaction quality overall. This special issue brings together a number of articles that tackle different aspects of spoken language understanding and interaction: clarifications in dialogues, adaptation to different domains, semantic tagging and error handling. These studies all have a common purpose of building human-like conversational systems.