{"id":163443,"date":"2011-01-01T00:00:00","date_gmt":"2011-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/spoken-language-understanding-systems-for-extracting-semantic-information-from-speech\/"},"modified":"2018-10-16T20:47:26","modified_gmt":"2018-10-17T03:47:26","slug":"spoken-language-understanding-systems-for-extracting-semantic-information-from-speech","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/spoken-language-understanding-systems-for-extracting-semantic-information-from-speech\/","title":{"rendered":"Spoken Language Understanding: Systems for Extracting Semantic Information from Speech"},"content":{"rendered":"

Spoken language understanding (SLU) is an emerging field in between speech and language processing, investigating human\/ machine and human\/ human communication by leveraging technologies from signal processing, pattern recognition, machine learning and artificial intelligence. SLU systems are designed to extract the meaning from speech utterances and its applications are vast, from voice search in mobile devices to meeting summarization, attracting interest from both commercial and academic sectors.<\/p>\n

Both human\/machine and human\/human communications can benefit from the application of SLU, using differing tasks and approaches to better understand and utilize such communications. This book covers the state-of-the-art approaches for the most popular SLU tasks with chapters written by well-known researchers in the respective fields. Key features include:<\/p>\n