Spoken Language Understanding: Systems for Extracting Semantic Information from SpeechJohn Wiley & Sons, 2011年5月3日 - 480 頁 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. 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:
This book can be successfully used for graduate courses in electronics engineering, computer science or computational linguistics. Moreover, technologists interested in processing spoken communications will find it a useful source of collated information of the topic drawn from the two distinct disciplines of speech processing and language processing under the new area of SLU. |
內容
| 7 | |
Semantic Framebased Spoken Language | |
Intent Determination and Spoken Utterance | |
Voice Search | |
Spoken Question Answering | |
SLU in Commercial and Research Spoken | |
Active Learning | |
HumanZHuman Conversation Understanding | |
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active learning algorithm analysis annotation applications approach ASR output ASR system Association for Computational audio automatic speech recognition broadcast classification Computational Linguistics concept conditional random fields Conference on Acoustics conversations corpora corpus database detection disfluencies domain error rate evaluation example extraction Figure grammar hypotheses ICASSP IEEE information retrieval input International Conference label language model lattice lexical Machine Learning meeting methods metrics n-best n-gram named entity named entity recognition natural language natural language processing parsing performance phonetic posterior probability probability problem Proc Proceedings prosodic query question answering robust sentence Shriberg Signal Processing similar slot speaker specific speech analytics speech summarization spoken dialogue systems spoken document spoken document retrieval Spoken Language Processing spoken language understanding statistical structure supervised learning syntactic task topic ID topic segmentation training data transcripts unsupervised utterance vector voice search word error rate word sequence Workshop