The development of English listening and speaking skills is essential for effective academic communication and global interaction, yet many learners face challenges because traditional learning environments fail to provide them with personalized feedback and objective evaluation methods. The goal of this research is to create an Artificial intelligence framework which automatically evaluates English speech performance through its assessment of multiple speech components including content and fluency and pronunciation. The proposed work introduces a hybrid model which combines Harris Hawks Optimization (HHO) for optimal acoustic feature selection with a Self-Attention Deep Neural Network (SA-DNN) for speech performance prediction. The Speech Content Fluency and Pronunciation Scores dataset provides speech recordings which undergo pre-processing through noise removal and silence trimming and normalization and resampling. The extraction process involves key acoustic features which include Mel-Frequency Cepstral Coefficients and pitch and energy and speech rate and pause duration and these features undergo optimization through HHO to eliminate any redundant elements. The SA-DNN model receives the optimized feature subset which uses Self-Attention to identify essential speech patterns that occur across different time intervals to determine multiple speech scoring dimensions. Experimental results demonstrate that the proposed SA-DNN with HHO model significantly outperforms baseline approaches which achieve Mean Absolute Error of 0.0307 and Mean Squared Error of 0.0019 and Root Mean Squared Error of 0.0431 and Coefficient of Determination (R2) of 0.975 which indicates high prediction accuracy and reliability. The proposed framework functions as an efficient and accurate automatic speech evaluation system which also enables intelligent language learning systems to operate.
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