Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation Scoring

Automatic pronunciation assessment models are regularly used in language learning applications. Common methodologies for pronunciation assessment use feature-based approaches, such as the Goodness-of-Pronunciation (GOP) approach, or deep learning speech recognition models to perform speech assessment. With the rise of transformers, pre-trained self-supervised learning (SSL) models have been utilized to extract contextual speech representations, showing improvements in various downstream tasks. In this study, we propose the end-to-end regressor (E2E-R) model for pronunciation scoring. E2E-R is trained using a two-step training process. In the first step, the pre-trained SSL model is fine-tuned on a phoneme recognition task to obtain better representations for the pronounced phonemes. In the second step, transfer learning is used to build a pronunciation scoring model that uses a Siamese neural network to compare the pronounced phoneme representations to embeddings of the canonical phonemes and produce the final pronunciation scores. E2E-R achieves a Pearson correlation coefficient (PCC) of 0.68, which is almost similar to the state-of-the-art GOPT-PAII model while eliminating the need for training on additional native speech data, feature engineering, or external forced alignment modules. To our knowledge, this work presents the first utilization of a pre-trained SSL model for end-to-end phoneme-level pronunciation scoring on raw speech waveforms.