System-independent ASR error detection and classification using Recurrent Neural Network. (May 2019)

• Record Type: Journal Article
• Authors: Errattahi, Rahhal; EL Hannani, Asmaa; Hain, Thomas; Ouahmane, Hassan
• Is Part Of: Computer speech & language. Volume 55(2019)
• Page Start: 187
• Page End: 199
• Publication Date: 2019-05
• Subjects: Automatic Speech Recognition -- ASR error detection -- ASR error type classification -- Recurrent Neural Network; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2018.12.007
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: A system independent approach for ASR error detection and classification is proposed. A set of generic features is used to train the ASR error detection and classification model. Variant Recurrent Neural Network based on label dependency is proposed as an effective classifier. The proposed system achieves competitive performances on the Multi-Genre Broadcast Media data as compared to the state-of-the-art approaches. Abstract: This paper addresses errors in continuous Automatic Speech Recognition (ASR) in two stages: error detection and error type classification. Unlike the majority of research in this field, we propose to handle the recognition errors independently from the ASR decoder. We first establish an effective set of generic features derived exclusively from the recognizer output to compensate for the absence of ASR decoder information. Then, we apply a variant Recurrent Neural Network (V-RNN) based models for error detection and error type classification. Such model learn additional information to the recognized word classification using label dependency. As a result, experiments on Multi-Genre Broadcast Media corpus have shown that the proposed generic features setup leads to achieve competitive performances, compared to state of the art systems in both tasks. Furthermore, we have shown that V-RNN trained on the proposed feature set appear to be an effective classifier for the ASR error detection with an Accuracy of 85.43%.