Assessing Pronunciation Improvement in Students of English Using a Controlled Computer-Assisted Pronunciation Tool
| Autor: | David Escudero-Mancebo, Enrique Cámara-Arenas, Valentín Cardeñoso-Payo, César González-Ferreras, Cristian Tejedor-García |
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| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
business.industry Computer science 05 social sciences Traditional classroom General Engineering Computer-Assisted Instruction Speech synthesis Pronunciation computer.software_genre 050105 experimental psychology Foreign language teaching Computer Science Applications Education Test (assessment) 0501 psychology and cognitive sciences Artificial intelligence Set (psychology) Hidden Markov model business computer Natural language processing |
| Zdroj: | IEEE Transactions on Learning Technologies. 13:269-282 |
| ISSN: | 2372-0050 |
| DOI: | 10.1109/tlt.2020.2980261 |
| Popis: | Over the last few years, we have witnessed a growing interest in computer-assisted pronunciation training (CAPT) tools and the commercial success of foreign language teaching applications that incorporate speech synthesis and automatic speech recognition technologies. However, empirical evidence supporting the pedagogical effectiveness of these systems remains scarce. In this article, a minimal-pair-based CAPT tool that implements exposure–perception–production cycles and provides automatic feedback to learners is tested for effectiveness in training adult native Spanish users (English level B1–B2) in the production of a set of difficult English sounds. Working under controlled conditions, a group of users took a pronunciation test before and after using the tool. Test results were considered against those of an in-classroom group who followed similar training within the traditional classroom setting. Results show a significant pronunciation improvement among the learners who used the CAPT tool, as well as a correlation between human rater's assessment of posttests and automatic CAPT assessment of users. |
| Databáze: | OpenAIRE |
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