An Empirical Study of Speech Recognition Errors in Human-Computer Dialogue

2003 ◽  
pp. 113-131
Author(s):  
Marc Cavazza
Keyword(s):  
Speech Recognition ◽  
Empirical Study ◽  
Recognition Errors

scholarly journals Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical Study

2020 ◽  
Author(s):  
Karthik Gopalakrishnan ◽  
Behnam Hedayatnia ◽  
Longshaokan Wang ◽  
Yang Liu ◽  
Dilek Hakkani-Tür
Keyword(s):  
Speech Recognition ◽  
Empirical Study ◽  
Open Domain ◽  
Dialog Systems ◽  
Recognition Errors

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%.

Feedback-Driven Refinement of Mandarin Speech Recognition Result Based on Lattice Modification and Rescoring

2020 ◽  
pp. 1237-1247
Author(s):  
Xiangdong Wang ◽  
Yang Yang ◽  
Hong Liu ◽  
Yueliang Qian ◽  
Duan Jia
Keyword(s):  
Speech Recognition ◽  
Real World ◽  
Computational Cost ◽  
User Feedback ◽  
Recognition Result ◽  
Novel Method ◽  
Mandarin Speech Recognition ◽  
Recognition Errors ◽  
Dynamic Updating ◽  
Manual Correction

In real world applications of speech recognition, recognition errors are inevitable, and manual correction is necessary. This paper presents an approach for the refinement of Mandarin speech recognition result by exploiting user feedback. An interface incorporating character-based candidate lists and feedback-driven updating of the candidate lists is introduced. For dynamic updating of candidate lists, a novel method based on lattice modification and rescoring is proposed. By adding words with similar pronunciations to the candidates next to the corrected character into the lattice and then performing rescoring on the modified lattice, the proposed method can improve the accuracy of the candidate lists even if the correct characters are not in the original lattice, with much lower computational cost than that of the speech re-recognition methods. Experimental results show that the proposed method can reduce 24.03% of user inputs and improve average candidate rank by 25.31%.

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