Automatic Speech Recognition Errors Detection Using Supervised Learning Techniques

Over the last years, many advances have been made in the field of Automatic Speech Recognition (ASR). However, the persistent presence of ASR errors is limiting the widespread adoption of speech technology in real life applications. This motivates the attempts to find alternative techniques to automatically detect and correct ASR errors, which can be very effective and especially when the user does not have access to tune the features, the models or the decoder of the ASR system or when the transcription serves as input to downstream systems like machine translation, information retrieval, and question answering. In this paper, we present an ASR errors detection system targeted towards substitution and insertion errors. The proposed system is based on supervised learning techniques and uses input features deducted only from the ASR output words and hence should be usable with any ASR system. Applying this system on TV program transcription data leads to identify 40.30% of the recognition errors generated by the ASR system.