Speech Classification Task

Speech classification involves automatically categorizing spoken audio into predefined classes, such as emotions, languages, or speaker identities. Current research focuses on improving accuracy and robustness, particularly in low-resource settings, by exploring techniques like semi-supervised learning, multimodal approaches combining audio and text features (often using transformer-based architectures like BERT and Wav2Vec 2.0), and prompt tuning methods. These advancements are crucial for applications ranging from improved voice assistants and healthcare diagnostics to environmental monitoring and wildlife research, where accurate and reliable speech analysis is essential.