MelHuBERT: A simplified HuBERT on Mel spectrograms

Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks. However, most self-supervised models require a large amount of compute and multiple GPUs to train, significantly hampering the development of self-supervised learning. In an attempt to reduce the computation of training, we revisit the training of HuBERT, a highly successful self-supervised model. We improve and simplify several key components, including the loss function, input representation, and training in multiple stages. Our model, MelHuBERT, is able to achieve favorable performance on phone recognition, speaker identification, and automatic speech recognition against HuBERT, while saving 31.2% of the pre-training time, or equivalently 33.5% MACs per one second speech. The code and pre-trained models are available in https://github.com/nervjack2/MelHuBERT.Comment: ASRU 202

Translators as gatekeepers : gender/race issues in three Taiwan translations of The color purple

Translation is regarded as a constrained activity (Boase-Beier ;Holman, 1999: 7). During the process of translation, there are inevitably factors that influence the translator. However, the factors influencing Taiwanese translators have rarely been investigated in translation studies. This is especially so of the time in the late 1980s when society, culture, and politics were in rapid transition. This study sets out to investigate potentially influential factors operating on Taiwanese translators during the translation process by considering three translations focusing on gender and race issues in the novel The Color Purple. Three versions were translated into Chinese in the same year, 1986. Such a rare occurrence gives us the opportunity to examine how these potentially influential factors, particularly the ones from the wider social context, affected each translation, and to draw wider implications for how translators tackled issues of gender and race in a socially sensitive context. The study adopts and modifies Chesterman's causal model (1992) as the theoretical framework; the study also uses Leuven-Zwart's transeme model (1989) and the concept of critical discourse analysis to investigate semantic shifts and ideological concerns in the gender and race issues in the three Taiwanese versions. Interviews are used to provide additional data. Our findings suggest that each translator, while tackling ideologies of anti-sexism and anti-racism in the original text, was influenced by individual factors, leading to divergent re-presentations. Nonetheless, rather than simply being influenced and conditioned, these variables to some extent empowered the translators to push the boundary of the prevailing attitudes in their translations. The translators' decisions on linguistic items, therefore, became their distinctive, personal responses to the target society, the translation field and the original.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

EXPLORING E-LEARNING BEHAVIOR THROUGH LEARNING DISCOURSES

As many studies predict e-learning behaviors through intention, few of them investigate user’s learning behaviors directly. In addition to intention, individual’s e-learning behaviors may be influenced by technology readiness and group influences, such as social identity and social bond. This research-in-progress study explores how e-learning behaviors vary with intention, technology readiness, social identity and social bond. Our investigation was based on analyzing the speech acts embedded in fourteen learners’ online discourses in an eighteen-week e-learning course. We then compared how speech acts varied among groups with different degree of intention, technology readiness, social identity, and social bond. Our findings contribute e-learning research by clarifying how intention, technology readiness, social identity, and social bond influence learning behaviors in e-learning context