MultiPA: a multi-task speech pronunciation assessment system for a
  closed and open response scenario
Yu-Wen Chen Zhou Yu Julia Hirschberg
The design of automatic speech pronunciation assessment can be categorized
into closed and open response scenarios, each with strengths and limitations. A
system with the ability to function in both scenarios can cater to diverse
learning needs and provide a more precise and holistic assessment of
pronunciation skills. In this study, we propose a Multi-task Pronunciation
Assessment model called MultiPA. MultiPA provides an alternative to Kaldi-based
systems in that it has simpler format requirements and better compatibility
with other neural network models. Compared with previous open response systems,
MultiPA provides a wider range of evaluations, encompassing assessments at both
the sentence and word-level. Our experimental results show that MultiPA
achieves comparable performance when working in closed response scenarios and
maintains more robust performance when directly used for open responses.