Multilingual Speech Representation

Multilingual speech representation research aims to create computational models that can understand and process spoken language across diverse languages, improving cross-lingual communication and enabling applications like speech translation and language identification. Current efforts focus on self-supervised learning methods, leveraging large multilingual datasets and architectures like HuBERT and wav2vec 2.0, to build robust and efficient models. These advancements are significant because they address the limitations of resource-intensive, language-specific approaches, paving the way for more inclusive and effective speech technologies, particularly for low-resource languages.