Enhancement of emotion detection in spoken dialogue systems by combining several information sources

2011 ◽  
Vol 53 (9-10) ◽  
pp. 1210-1228 ◽  
Author(s):  
Ramón López-Cózar ◽  
Jan Silovsky ◽  
Martin Kroul
Keyword(s):  
Information Sources ◽  
Dialogue Systems ◽  
Emotion Detection ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue

scholarly journals Automatically Training a Problematic Dialogue Predictor for a Spoken Dialogue System

2002 ◽  
Vol 16 ◽  
pp. 293-319 ◽  
Author(s):  
M. A. Walker ◽  
I. Langkilde-Geary ◽  
H. Wright Hastie ◽  
J. Wright ◽  
A. Gorin
Keyword(s):  
Information Sources ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Customer Care ◽  
Dialogue Manager

Spoken dialogue systems promise efficient and natural access to a large variety of information sources and services from any phone. However, current spoken dialogue systems are deficient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic human-computer dialogues using a corpus of 4692 dialogues collected with the 'How May I Help You' (SM) spoken dialogue system. The Problematic Dialogue Predictor can be immediately applied to the system's decision of whether to transfer the call to a human customer care agent, or be used as a cue to the system's dialogue manager to modify its behavior to repair problems, and even perhaps, to prevent them. We show that a Problematic Dialogue Predictor using automatically-obtainable features from the first two exchanges in the dialogue can predict problematic dialogues 13.2% more accurately than the baseline.

SemEval-2014 Task 2: Grammar Induction for Spoken Dialogue Systems

2014 ◽  
Author(s):  
Ioannis Klasinas ◽  
Elias Iosif ◽  
Katerina Louka ◽  
Alexandros Potamianos
Keyword(s):  
Dialogue Systems ◽  
Grammar Induction ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue

Spoken dialogue systems

2014 ◽  
Vol 21 (1) ◽  
pp. 46-51 ◽  
Author(s):  
Pierre Lison ◽  
Raveesh Meena
Keyword(s):  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue

scholarly journals Characterizing and Predicting Corrections in Spoken Dialogue Systems

2006 ◽  
Vol 32 (3) ◽  
pp. 417-438 ◽  
Author(s):  
Diane Litman ◽  
Julia Hirschberg ◽  
Marc Swerts
Keyword(s):  
Speech Recognition ◽  
Predictive Power ◽  
Classification Error ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Spoken Dialogue Systems ◽  
Experimental Conditions ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Recognition Errors

This article focuses on the analysis and prediction of corrections, defined as turns where a user tries to correct a prior error made by a spoken dialogue system. We describe our labeling procedure of various corrections types and statistical analyses of their features in a corpus collected from a train information spoken dialogue system. We then present results of machine-learning experiments designed to identify user corrections of speech recognition errors. We investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. Our best-performing features reduce classification error from baselines of 25.70–28.99% to 15.72%.

Real user evaluation of spoken dialogue systems using Amazon Mechanical Turk

2011 ◽  
Author(s):  
F. Jurčíček ◽  
S. Keizer ◽  
Milica Gašić ◽  
François Mairesse ◽  
B. Thomson ◽  
...  
Keyword(s):  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
User Evaluation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue
Sign in / Sign up

Export Citation Format

Share Document