Speakometer: English Pronunciation Coach

Abstract

Consonant and vowel sounds of English (segmentals) carry a significant weight in communication. Pronunciation instruction focusing on segmental features has been found to be highly effective (e.g., Thomson & Derwing, 2015). However, students with different first languages (L1) or even students from the same L1 backgrounds, have different pronunciation needs. With limited class time, teachers cannot be expected to cater to the pronunciation needs of every student. This has made individualized pronunciation instruction, which enables pronunciation instruction tailored for the needs of each second language (L2) learner, a requirement in today’s language classrooms (Levis, 2007). The growing number of computer-assisted pronunciation training (CAPT) tools have been responding to this need, making individualized pronunciation instruction, as well as individualized feedback more feasible and available for L2 speakers. Chun (2012) asserts that in order for a CAPT tool to be effective, it must contain “auditory and visualization features, automatic speech recognition (ASR), and appropriate and accurate feedback” (p. 8). Speakometer, an online application that provides segmental practice for its users, was built around Chun’s (2012) three pillars, with a strong auditory feature combined with an ASR to provide learners with relevant pronunciation feedback. The application uses an artificial intelligence (AI) algorithm and ASR to rate the user’s spoken English pronunciation. It is targeted for all users who aim to improve their English pronunciation. The users are provided with immediate feedback, which appears on the screen as verbal (e.g., “Very good”), along with the image of a ‘speakometer’ displaying four colors for the rating: red, orange, yellow and green.