Multilingual Speech Model

Multilingual speech models aim to build single systems capable of processing and understanding speech across many languages, improving accessibility and efficiency in applications like speech recognition and translation. Current research focuses on adapting pre-trained models like Whisper and wav2vec 2.0 to low-resource languages, often employing techniques like lightweight adapters (e.g., LoRA), knowledge distillation, and self-supervised learning to enhance performance and mitigate issues like catastrophic forgetting and bias. These advancements are significant because they enable more inclusive and efficient development of speech technologies, particularly for under-resourced languages and dialects, and facilitate cross-lingual transfer learning for improved performance in various downstream tasks.