An Effective End-to-End Modeling Approach for Mispronunciation Detection

Understand

Recently, end-to-end (E2E) automatic speech recognition (ASR) systems have garnered tremendous attention because of their great success and unified modeling paradigms in comparison to conventional hybrid DNN-HMM ASR systems.

• Despite the widespread adoption of E2E modeling frameworks on ASR, there still is a dearth of work on investigating the E2E frameworks for use in computer-assisted pronunciation learning (CAPT), particularly for Mispronunciation detection (MD).
• In response, we first present a novel use of hybrid CTCAttention approach to the MD task, taking advantage of the strengths of both CTC and the attention-based model meanwhile getting around the need for phone-level forced alignment.
• Second, we perform input augmentation with text prompt information to make the resulting E2E model more tailored for the MD task.