The article presents an innovative approach to dysarthric speech reconstruction (DSR) using a system called Unit-DSR. This system uses speech units and HuBERT for domain-adaptation capacity, improving training efficiency. Compared…
Continue readingThe research explores the use of models such as BERT and HuBERT in dimensional speech emotion recognition. Despite their large dimensionality, these models are frequently used for emotion recognition tasks,…
Continue readingThis research article discusses the development of a frame-level emotional state alignment method for speech emotion recognition (SER). The authors fine-tune the HuBERT model to create a SER system with…
Continue readingThe study by Geetanjali Rakshit and Jeffrey Flanigan investigates the robustness of Question Answering (QA) models on figurative text. The researchers propose FigurativeQA, a set of 1000 yes/no questions with…
Continue readingThe article discusses the development of Fast-HuBERT, an efficient training framework for self-supervised learning (SSL) models for speech recognition. The authors address the high computational cost of existing SSL models,…
Continue readingThis article provides a detailed overview of Named Entity Recognition (NER), a key aspect of Natural Language Processing (NLP) that identifies and classifies named entities in text. The author explores…
Continue readingThis article provides an in-depth exploration into Named Entity Recognition (NER) methods, from traditional rule-based strategies to modern transformer architectures like BERT, LSTM, and CNN. The study emphasizes domain-specific NER…
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