HOW WELL CAN ASR TECHNOLOGY UNDERSTAND FOREIGN-ACCENTED SPEECH?
Keywords:intelligibility, automatic speech recognition, L2 pronunciation development, autonomous learning
Following the Covid-19 pandemic, digital technology is more present in classrooms than ever. Automatic Speech Recognition (ASR) offers interesting possibilities for language learners to produce more output in a foreign language (FL). ASR is especially suited for autonomous pronunciation learning when used as a dictation tool that transcribes the learner’s speech (McCROCKLIN, 2016). However, ASR tools are trained with monolingual native speakers in mind, not reflecting the global reality of English speakers. Consequently, the present study examined how well two ASR-based dictation tools understand foreign-accented speech, and which FL speech features cause intelligibility breakdowns. English speech samples of 15 Brazilian Portuguese and 15 Spanish speakers were obtained from an online database (WEINBERGER, 2015) and submitted to two ASR dictation tools: Microsoft Word and VoiceNotebook. The resulting transcriptions were manually inspected, coded and categorized. The results show that overall intelligibility was high for both tools. However, many features of normal FL speech, such as vowel and consonant substitution, caused the ASR dictation tools to misinterpret the message leading to communication breakdowns. The results are discussed from a pedagogical viewpoint.
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Copyright (c) 2022 Hanna Kivistö Souza, William Gottardi
This work is licensed under a Creative Commons Attribution 4.0 International License.