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Using Paralinguistic Information to Disambiguate User Intentions for Distinguishing Phrase Structure and Sarcasm in Spoken Dialog Systems
- Source :
- SLT
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- This paper aims at utilizing paralinguistic information usually hidden in speech signals, such as pitch, short pause and sarcasm, to disambiguate user intention not easily distinguishable from speech recognition and natural language understanding results provided by a state-of-the-art spoken dialog system (SDS). We propose two methods to address the ambiguities in understanding name entities and sentence structures based on relevant speech cues and nuances. We also propose an approach to capturing sarcasm in speech and generating sarcasm-sensitive responses using an end-to-end neural network. An SDS prototype that directly feeds signal information into the understanding and response generation components has also been developed to support the three proposed applications. We have achieved encouraging experimental results in this initial study, demonstrating the potential of this new research direction.
- Subjects :
- Artificial neural network
Sarcasm
Computer science
business.industry
media_common.quotation_subject
Phrase structure rules
Natural language understanding
020206 networking & telecommunications
02 engineering and technology
computer.software_genre
Paralanguage
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Natural language
Natural language processing
Sentence
Spoken dialog systems
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2021 IEEE Spoken Language Technology Workshop (SLT)
- Accession number :
- edsair.doi...........1b6ab17aa2a06ad584191cc39ead590b
- Full Text :
- https://doi.org/10.1109/slt48900.2021.9383505