Mispronunciation detection and diagnosis for Mandarin accented English speech

This paper presents a Mispronunciation Detection and Diagnosis (MDD) system based on a range of Automatic Speech Recognition (ASR) models and feature types. The goals of this research are to assess the ability of speech recognition systems to detect and diagnose the common pronunciation errors seen in non-native speakers (L2) of English and to assess the contribution of the information offered by Electromagnetic Articulography (EMA) data in improving the performance of such MDD systems. To evaluate the ability of the ASR systems to detect and diagnose pronunciation errors, the recognized sequence of phonemes generated by the ASR models were aligned with human-labeled phonetic transcripts as well as with the original phonetic prompts. This three-way alignment determined the MDD related metrics of the ASR system. System architectures included GMM-HMM, DNN, and RNN based ASR engines for the MDD system. Articulatory features derived from the Electromagnetic Articulography corpus of Mandarin-Accented English (EMA-MAE) were utilized along with acoustic features to compare the performance of MDD systems. The best performing system using a combination of acoustic and articulatory features had an accuracy of 82.4%, diagnostic accuracy of 75.8% and a false rejection rate of 17.2%.