Transforming English language learning: Advanced speech recognition with MLP-LSTM for personalized education - Myagmarsuren Orosoo; Namjildagva Raash; Mark Treve; Hassan Fareed M. Lahza; Nizal Alshammry; Janjhyam Venkata Naga Ramesh; Manikandan Rengarajan (Alexandria Engineering Journal)
Speaking of speech recognition within the English language, it is the process of recognizing oral speech and transcribing it into writing using exclusive algorithms. For the perishable skill of English language learning, use of innovative speech recognition technology using Advanced Speech Recognition Technologies MLP-LSTM is proposed in this paper to advance the existing online learning platforms. Previous research addresses the importance of NLP in English language learning but notes the challenges in effectively extracting and segmenting features from multimodal data. In order to overcome these problems, this paper incorporate the proposed MLP for feature extraction and LSTM for sequence learning. The utilization of MLP-LSTM provides not only a brilliant improvement of the capacity to transform spoken language and perceive it but also minimizes the Word Error Rate (WER) to 0.075. With this low WER, along with the total accuracy rate of 98.25 %, this paper focus on underlining how this system is more effective than traditional language learning tools. This paper has been implemented through Python Software. The given MLP-LSTM based speech recognition model lays the foundation for a highly complex yet accurate paced English language learning platform that will cater to the needs of the learners in the global scenario.