Effects of Speech Recognition Accuracy on the Performance of DARPA Communicator Spoken Dialogue Systems

Mapping Intimacies ◽

10.21236/ada523241 ◽

2004 ◽

Author(s):

Gregory A. Sanders ◽

Audrey N. Le

Keyword(s):

Speech Recognition ◽

Recognition Accuracy ◽

Dialogue Systems ◽

Spoken Dialogue Systems ◽

Spoken Dialogue

Download Full-text

Effects of Speech Recognition Accuracy on the Performance of DARPA Communicator Spoken Dialogue Systems

International Journal of Speech Technology ◽

10.1023/b:ijst.0000037073.52437.27 ◽

2004 ◽

Vol 7 (4) ◽

pp. 293-309

Author(s):

Gregory A. Sanders ◽

Audrey N. Le

Keyword(s):

Speech Recognition ◽

Recognition Accuracy ◽

Dialogue Systems ◽

Spoken Dialogue Systems ◽

Spoken Dialogue

Download Full-text

Characterizing and Predicting Corrections in Spoken Dialogue Systems

Computational Linguistics ◽

10.1162/coli.2006.32.3.417 ◽

2006 ◽

Vol 32 (3) ◽

pp. 417-438 ◽

Cited By ~ 19

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%.

Download Full-text