12/20/2023 0 Comments Computer speek![]() An experimental research project with a pretest-posttest design is conducted over a one-month period in the Department of English at Al-Turath University College in Baghdad, Iraq. The purpose of this study is to investigate the effectiveness of using automatic speech recognition ASR EyeSpeak software in improving the pronunciation of Iraqi learners of English. Thus, the sound transformation from the mother tongue (Arabic) to the target language (English) is one barrier for Arab learners. One factor contributing to these mistakes is the difference between the Arabic and English phonetic systems. In Iraq, English is a foreign language, and it is not surprising that learners commit many pronunciation mistakes. Integrating such technology, especially in the instruction of pronunciation in the classroom, is important in helping students to achieve correct pronunciation. One promising emerging technology that supports language learning is automatic speech recognition (ASR). The use of technology, such as computer-assisted language learning (CALL), is used in teaching and learning in the foreign language classrooms where it is most needed. In addition, the system will help teachers impart basic reading skills to assist students in comprehensive development. The objectives of this paper is designing, implementing, and testing a prototype system that can add, and edit additional phonetic topics to cover pronunciation errors in teaching-based activities for adult students. The proposed editor framework is optimized to suit an embedded phonetic pronunciation database and is useful for analyzing and detecting speech errors in Arabian region. The core engine of the Speak Correct was trained using prerecorded 100 hours of speech and used these data to create a pronunciation-training database. The two levels cover detailed accent defects that describe such articulation. Two levels of teaching will be implemented consonants and vowels, which are important for speech recognition. Speak Correct system will be presented, which uses state of the art automatic speech recognition (ASR) and examines pronunciation errors by speech engine. Methods and the architecture of systems used to edit new lessons into proposed dictionary will be discussed taken into consideration pronunciation effects. In this paper, phonetic editor system for learning English speaking will be introduced.
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