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The design of automatic speech pronunciation assessment can be categorizedinto closed and open response scenarios, each with strengths and limitations. Asystem with the ability to function in both scenarios can cater to diverselearning needs and provide a more precise and holistic assessment ofpronunciation skills. In this study, we propose a Multi-task PronunciationAssessment model called MultiPA. MultiPA provides an alternative to Kaldi-basedsystems in that it has simpler format requirements and better compatibilitywith other neural network models. Compared with previous open response systems,MultiPA provides a wider range of evaluations, encompassing assessments at boththe sentence and word-level. Our experimental results show that MultiPAachieves comparable performance when working in closed response scenarios andmaintains more robust performance when directly used for open responses.

MultiPA: a multi-task speech pronunciation assessment system for a closed and open response scenario