Universal Speech Model

Universal Speech Models (USMs) aim to create single, large-scale models capable of handling diverse speech tasks across multiple languages and domains, improving efficiency and generalization compared to task-specific models. Current research focuses on improving USM performance through techniques like instruction tuning, model compression (e.g., quantization and sparsity), and integrating large language models for improved accuracy and reduced latency, often utilizing architectures such as RNN-T and Conformers. These advancements are significant because they promise more efficient and robust speech processing applications, impacting fields like automatic speech recognition, speaker diarization, and even the detection of speech abnormalities related to neurological disorders.