AI Enhancements for Linguistic E-Learning System

Abstract

E-learning systems have been considerably developed after the COVID-19 pandemic. In our previous work, we developed a linguistic interactive E-learning system for phonetic transcription learning. In this paper, we propose three artificial-intelligence-based enhancements to this system from different aspects. Compared with the original system, the first enhancement is a disordered speech classification module; this module is driven by the MFCC-CNN model, which aims to distinguish disordered speech and nondisordered speech. The accuracy of the classification is about 83%. The second enhancement is a grapheme-to-phoneme converter. This converter is based on the transformer model and designed for teachers to better generate IPA words from the regular written text. Compared with other G2P models, our transformer-based G2P model provides outstanding PER and WER performance. The last part of this paper focuses on a Tacotron2-based IPA-to-speech synthesis system, this deep learning-based TTS system can help teacher generate high-quality speech sounds from IPA characters which significantly improve the functionality of our original system. All of these three enhancements are related to the phonetic transcription process. and this work not only provides a better experience for the users of this system but also explores the utilization of artificial intelligence technologies in the E-learning field and linguistic field.

Keywords

• linguistic E-learning
• phonetic transcription
• Mel frequency cepstrum coefficient
• grapheme-to-phoneme
• transformer
• speech synthesis