# AI Enhance 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. linguistic E-learning; phonetic transcription; Mel frequency cepstrum coefficient; grapheme-to-phoneme; transformer; speech synthesis +
Footnote †: journal: _aciences_  
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Our study mainly focused on the teacher part and aimed at improving the experience of teachers. The prototype of the linguistic system was designed for the Communication Disorder Department. Teachers always require large amounts of time to distinguish disordered speech from nondisordered speech. Moreover, phonetic transcription is a process that generates words in phonetic format (IPA characters) from their written format. For teachers, adding questions in the system also requires them to add the corresponding correct answer in phonetic format. A grapheme-to-phoneme converter assists teachers in finishing this step and significantly improves the user experience. In the last part, the phonetic transcription exams in our system require the speech sound of English words or brief phrases; using the recordings of students or staff may cause privacy issues. A high-quality speech synthesis system perfectly solves this problem. All of the above challenges inspired us to build the following enhancements for our E-learning system. The three enhancements that we propose include the following:  
* A MFCC+CNN-based disordered speech classification module. * A Transformer-based grapheme-to-phoneme (G2P) converter module. * A Tacotron2-based IPA-to-speech synthesis module. The disordered speech classification module aims to to distinguish disordered speech from nondisordered speech. As an intelligent linguistic E-learning system, this module helps students understand the difference between correct pronunciation and disordered speech. During this study, we employed the Mel frequency cepstrum coefficient (MFCC) as the feature to represent the speech sound and a convolutional neural network (CNN) as the classification model [5]. The second enhancement module for our E-learning system is a transformer-based grapheme-to-phoneme (G2P) converter. In the phonetic transcription exam, teachers need to upload the pronunciation of English words, phrases, and sentences, where the pronunciation of speech sounds can be represented as phonemes. G2P conversion is a process that converts written words (grapheme) to their pronunciations (phonemes) [6]. With this G2P converter, teachers can easily extract the words in their phonetic format.