Empowering Spoken English Assessment: Advanced Deep Learning Techniques for Automated Evaluation

The Empowering Spoken English Assessment system represents a novel approach to automated evaluation of spoken English proficiency, leveraging advanced deep learning techniques. This system comprises several interconnected components aimed at efficiently processing and analyzing spoken English samples to provide accurate and comprehensive assessments. The data collection phase involves sourcing diverse spoken English samples from various outlets, meticulously transcribing them, and annotating them with metadata such as speaker demographics and proficiency levels. Expert evaluations further enrich the dataset, providing reference scores for model validation and refinement. A comprehensive speech data preprocessing pipeline ensures the quality and consistency of the dataset by performing tasks such as data cleaning, feature extraction, normalization, and data augmentation. The speech feature extraction method employs spectrogram analysis, MFCC calculation, and prosodic feature extraction to capture nuances in pronunciation, intonation, and rhythm. These extracted features serve as input to a multi-modal deep learning architecture, which integrates Convolutional Neural Networks and Long Short-Term Memory networks. Through attention mechanisms and fusion layers, this architecture effectively combines information from spectrograms, MFCCs, and prosodic features, enabling accurate assessment of language proficiency. The system’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, demonstrating its effectiveness in automated spoken English assessment. Future work may focus on enhancing the system’s adaptability and expanding its applicability to diverse linguistic contexts and educational settings.