Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection

Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection

Abstract Speech based human-machine interaction and natural language understanding applications have seen a rapid development and wide adoption over the last few decades. This has led to a proliferation of studies that investigate Error detection and classification in Automatic Speech Recognition (A...

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Bibliographic Details
Main Authors: Asmaa El Hannani, Rahhal Errattahi, Fatima Zahra Salmam, Thomas Hain, Hassan Ouahmane
Format: Article
Language:English
Published: SpringerOpen 2021-01-01
Series:Journal of Big Data
Subjects:
Automatic Speech Recognition
Confidence estimation
ASR error detection
ASR error type classification
Recurrent Neural Network
Multi-Genre Broadcast
Online Access:https://doi.org/10.1186/s40537-020-00391-w

Internet

https://doi.org/10.1186/s40537-020-00391-w

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