Multilingual Speech

Multilingual speech research aims to develop computational models capable of understanding and generating speech across many languages, overcoming the limitations of monolingual systems. Current efforts focus on improving speech recognition and synthesis using self-supervised learning, large language models (LLMs), and novel architectures like diffusion models and transformer-based approaches, often incorporating techniques like weighted cross-entropy and adapter fine-tuning to handle low-resource languages. These advancements are crucial for bridging language barriers in applications such as multilingual assistants, translation, and accessibility technologies, impacting both the scientific understanding of speech processing and its practical deployment globally. The creation and utilization of large, diverse multilingual speech datasets are also key to progress in the field.