Automatic speech recognition in computer-assisted language learning for individual learning in speaking
نویسندگان
چکیده
منابع مشابه
Teaching approaches to Computer Assisted Language Learning
Computers have been used for language teaching ever since the 1960's.Learning a second language is a challenging endeavor, and, for decades now, proponents of computer assisted language learning (CALL) have declared that help is on the horison. We investigate the suitability of deploying speech technology in computer based systems that can be used to teach foreign language skills. In this case,...
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چکیده انگیزه دلیل اصلی رفتارهای ما است. به نظر می رسد انگیزه جزء جدایی ناپذیر فرایند یادگیری باشد. ارزش ذاتی موفقیت تمایل به پیشرفت را در یادگیرنده ایجاد میکند. به عبارت ساده این تمایل انگیزه پیشرفت نامیده میشود. انگیزه پیشرفت را میتوان در احساس یادگیرنده هنگام چالش با درس های مدرسه، لذت انجام فعالیت درسی، یا حس کشف پاسخ مشاهده کرد.حتی ممکن است انگیزه پیشرفت را در تلاش یادگیرنده برای جلب تایید...
Arabic Language Learning Assisted by Computer, based on Automatic Speech Recognition
This work consists of creating a system of the Computer Assisted Language Learning (CALL) based on a system of Automatic Speech Recognition (ASR) for the Arabic language using the tool CMU Sphinx3 [1], based on the approach of HMM. To this work, we have constructed a corpus of six hours of speech recordings with a number of nine speakers. we find in the robustness to noise a grounds for the cho...
متن کاملSpeech-Adaptive Time-Scale Modification for Computer Assisted Language-Learning
In traditional foreign language learning programmes students are offered a tutor model characterised by slow, deliberate speech. This is insufficient to prepare them to cope with native, colloquial speech. By applying a timescale modification (TSM) algorithm to natural-speed, native speech, students have access to a more desirable, natural speech corpus which permits them to practise essential ...
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ژورنال
عنوان ژورنال: JEES (Journal of English Educators Society)
سال: 2020
ISSN: 2503-3492
DOI: 10.21070/jees.v5i2.867