Using Paralinguistic Information to Disambiguate User Intentions for Distinguishing Phrase Structure and Sarcasm in Spoken Dialog Systems

2021 ◽  
Author(s):  
Zhengyu Zhou ◽  
In Gyu Choi ◽  
Yongliang He ◽  
Vikas Yadav ◽  
Chin-Hui Lee
Keyword(s):  
Phrase Structure ◽  
Spoken Dialog Systems ◽  
Dialog Systems ◽  
User Intentions ◽  
Paralinguistic Information

scholarly journals Using finite state machines for evaluating spoken dialog systems

2010 ◽  
Author(s):  
Yi Zhu ◽  
Zhaojun Yang ◽  
Helen Meng ◽  
Baichuan Li ◽  
Gina Levow ◽  
...  
Keyword(s):  
Finite State Machines ◽  
State Machines ◽  
Spoken Dialog Systems ◽  
Dialog Systems ◽  
Finite State

Large-scale experiments on data-driven design of commercial spoken dialog systems

2011 ◽  
Author(s):  
D. Suendermann ◽  
J. Liscombe ◽  
J. Bloom ◽  
G. Li ◽  
Roberto Pieraccini
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Spoken Dialog Systems ◽  
Dialog Systems

Dynamic language modeling using Bayesian networks for spoken dialog systems

2010 ◽  
Author(s):  
Antoine Raux ◽  
Neville Mehta ◽  
Deepak Ramachandran ◽  
Rakesh Gupta
Keyword(s):  
Bayesian Networks ◽  
Language Modeling ◽  
Spoken Dialog Systems ◽  
Dialog Systems ◽  
Dynamic Language

Avatar and Dialog Turn-Yielding Phenomena

2013 ◽  
Vol 9 (2) ◽  
pp. 66-88 ◽  
Author(s):  
Ladislav Kunc ◽  
Zdenek Míkovec ◽  
Pavel Slavík
Keyword(s):  
Discourse Structure ◽  
Conversational Agent ◽  
Correct Judgment ◽  
Spoken Dialog Systems ◽  
Dialog Systems ◽  
Vocal Cues ◽  
Synthesized Speech ◽  
Turn Taking

Turn-taking and turn-yielding phenomena in dialogs receive increasing attention nowadays. A growing number of spoken dialog systems inspire application designers to humanize people’s interaction experience with computers. The knowledge of psychology in discourse structure could be helpful in this effort. In this paper the authors explore effectiveness of selected visual and vocal turn-yielding cues in dialog systems using synthesized speech and an avatar. The aim of this work is to detect the role of visual and vocal cues on dialog turn-change judgment using a conversational agent. The authors compare and study the cues in two experiments. Findings of those experiments suggest that the selected visual turn-yielding cues are more effective than the vocal cues in increasing correct judgment of dialog turn-change. Vocal cues in the experiment show quite poor results and the conclusion discusses possible explanations of that.

scholarly journals The Dialog State Tracking Challenge Series

AI Magazine ◽  
2014 ◽  
Vol 35 (4) ◽  
pp. 121-124 ◽  
Author(s):  
Jason D. Williams ◽  
Matthew Henderson ◽  
Antoine Raux ◽  
Blaise Thomson ◽  
Alan Black ◽  
...  
Keyword(s):  
Research Community ◽  
Spoken Dialog Systems ◽  
Dialog Systems ◽  
New Methods ◽  
State Tracking ◽  
Dialog State Tracking

In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. The Dialog State Tracking Challenge is a research community challenge task that has run for three rounds. The challenge has given rise to a host of new methods for dialog state tracking, and also deeper understandings about the problem itself, including methods for evaluation.

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