Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment

• URL: http://arxiv.org/abs/2205.03432v1
• Date: Fri, 6 May 2022 18:07:44 GMT
• Title: Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment
• Authors: Yuan Gong, Ziyi Chen, Iek-Heng Chu, Peng Chang, James Glass
• Abstract summary: We train a Goodness Of Pronunciation feature-based Transformer (GOPT) with multi-task learning. Experiments show that GOPT achieves the best results on speechocean762 with a public automatic speech recognition (ASR) acoustic model trained on Librispeech.
• Score: 10.809349710149533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic pronunciation assessment is an important technology to help self-directed language learners. While pronunciation quality has multiple aspects including accuracy, fluency, completeness, and prosody, previous efforts typically only model one aspect (e.g., accuracy) at one granularity (e.g., at the phoneme-level). In this work, we explore modeling multi-aspect pronunciation assessment at multiple granularities. Specifically, we train a Goodness Of Pronunciation feature-based Transformer (GOPT) with multi-task learning. Experiments show that GOPT achieves the best results on speechocean762 with a public automatic speech recognition (ASR) acoustic model trained on Librispeech.

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