Cámara-Arenas, Enrique Tejedor-García, Cristian Tomas-Vázquez, Cecilia Judith Escudero-Mancebo, David
This study addresses the issue of automatic pronunciation assessment (APA) and its contribution to the teaching of second language (L2) pronunciation. Several attempts have been made at designing such systems, and some have proven operationally successful. However, the automatic assessment of the pronunciation of short words in segmental approaches has still remained a significant challenge. Free and off-the-shelf automatic speech recognition (ASR) systems have been used in integration with other tools with the hopes of facilitating improvement in the domain of computer-assisted pronunciation
training (CAPT). The use of ASR in APA stands on the premise that a word that is recognized is
intelligible and well-pronounced. Our goal was to explore and test the functionality of Google ASR as the core component within a possible automatic British English pronunciation assessment system. After testing the system against standard and non-standard (foreign) pronunciations provided by participating pronunciation experts as well as non-expert native and non-native speakers of English, we found that Google ASR does not and cannot simultaneously meet two necessary conditions (here defined as intrinsic and derived) for performing as an APA system. Our study concludes with a synthetic view on the requirements of a reliable APA system.
Cámara-Arenas, E., Tejedor-García, C., Tomas-Vázquez, C. J., & Escudero-Mancebo, D. (2023). Automatic pronunciation assessment vs. automatic speech recognition: A study of conflicting conditions for L2-English. Language Learning & Technology, 27(1), 1–19. https://hdl.handle.net/10125/73512
Language Learning & Technology
University of Hawaii National Foreign Language Resource Center Center for Language & Technology
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