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Automatic pronunciation assessment (APA) plays an important role in providing feedback for self-directed language learners in computer-assisted pronunciation training (CAPT). Several mispronunciation detection and diagnosis (MDD) systems have achieved promising performance based on end-to-end phoneme recognition. However, assessing the intelligibility of second language (L2) remains a challenging problem. One issue is the lack of large-scale labeled speech data from non-native speakers. Additionally, relying only on one aspect (e.g., accuracy) at a phonetic level may not provide a sufficient assessment of pronunciation quality and L2 intelligibility. It is possible to leverage segmental/phonetic-level features such as goodness of pronunciation (GOP), however, feature granularity may cause a discrepancy in prosodic-level (suprasegmental) pronunciation assessment. In this study, Wav2vec 2.0-based MDD and Goodness Of Pronunciation feature-based Transformer are employed to characterize L2 intelligibility. Here, an L2 speech dataset, with human-annotated prosodic (suprasegmental) labels, is used for multi-granular and multi-aspect pronunciation assessment and identification of factors important for intelligibility in L2 English speech. The study provides a transformative comparative assessment of automated pronunciation scores versus the relationship between suprasegmental features and listener perceptions, which taken collectively can help support the development of instantaneous assessment tools and solutions for L2 learners. 

This study is focused on understanding and quantifying the change in phoneme and prosody information encoded in the Self-Supervised Learning (SSL) model, brought by an accent identification (AID) fine-tuning task. This problem is addressed based on model probing. Specifically, we conduct a systematic layer-wise analysis of the representations of the Transformer layers on a phoneme correlation task, and a novel word-level prosody prediction task. We compare the probing performance of the pre-trained and fine-tuned SSL models. Results show that the AID fine-tuning task steers the top 2 layers to learn richer phoneme and prosody representation. These changes share some similarities with the effects of fine-tuning with an Automatic Speech Recognition task. In addition, we observe strong accent-specific phoneme representations in layer 9. To sum up, this study provides insights into the understanding of SSL features and their interactions with fine-tuning tasks. 

This study investigates the relationships of learner background variables of adult English for Speakers of Other Languages (ESOL) learners and a mobile App designed to promote pronunciation skills targeting features known to contribute to intelligibility. Recruited from free evening classes for English learners, 34 adult ESOL learners of mixed ESOL learning experiences, ages, lengths of residency, and first languages (L1s) completed six phoneme pair lessons on a mobile App along with a background questionnaire and technology acceptance survey (Venkatesh et al., 2012). A series of Linear Mixed-Effect Model (LMEM) analyses were performed on learner background variables, technology acceptance, learner effort, and accuracy. The results found a minimal relationship between age, technology acceptance, and effort (7.68%) but a moderate to large relationship between age, technology acceptance and accuracy of consonants (39.70%) and vowels (64.26%). The implications are that learner use of mobile devices for L2 pronunciation training is moderated by various learner-related factors and the findings offer supportive evidence for designing mobile-based applications for a wide variety of backgrounds. 

This study investigates the relationships of learner background variables of adult English for Speakers of Other Languages (ESOL) learners and a mobile App designed to promote pronunciation skills targeting features known to contribute to intelligibility. Recruited from free evening classes for English learners, 34 adult ESOL learners of mixed ESOL learning experiences, ages, lengths of residency, and first languages (L1s) completed six phoneme pair lessons on a mobile App along with a background questionnaire and technology acceptance survey (Venkatesh et al., 2012). A series of Linear Mixed-Effect Model (LMEM) analyses were performed on learner background variables, technology acceptance, learner effort, and accuracy. The results found a minimal relationship between age, technology acceptance, and effort (7.68%) but a moderate to large relationship between age, technology acceptance and accuracy of consonants (39.70%) and vowels (64.26%). The implications are that learner use of mobile devices for L2 pronunciation training is moderated by various learner-related factors and the findings offer supportive evidence for designing mobile-based applications for a wide variety of backgrounds.