Abstract
Any natural language may have dozens of accents. Even though the equivalent phonemic formation of the word, if it is properly called in different accents, humans do have audio signals that are distinct from one another. Among the most common issues with speech, the processing is discrepancies in pronunciation, accent, and enunciation. This research study examines the issues of detecting, fixing, and summarising accent defects of average Arabic individuals in English-speaking speech. The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues. The proposed SpeakCorrect computerized interface employs a cuttingedge speech recognition system and analyses pronunciation errors with a speech decoder. As a result, some of the most essential types of changes in pronunciation that are significant for speech recognition are performed, and accent defects defining such differences are presented. Consequently, the suggested technique increases the Speaker???s accuracy. SpeakCorrect uses 100 h of phonetically prepared individuals to construct a pronunciation instruction repository. These prerecorded sets are used to train Hidden Markov Models (HMM) as well as weighted graph systems. Their speeches are quite clear and might be considered natural. The proposed interface is optimized for use with an integrated phonetic pronounced dataset, as well as for analyzing and identifying speech faults in Saudi and Egyptian dialects. The proposed interface detects, analyses, and assists English learners in correcting utterance faults, overcoming problems, and improving their pronunciations.