Serialized Output Training

Serialized Output Training (SOT) is a technique for simplifying multi-speaker speech recognition by sequentially concatenating speaker transcriptions into a single output stream. Current research focuses on improving SOT's performance using various approaches, including integrating connectionist temporal classification (CTC) losses, leveraging large language models (LLMs) for improved context modeling, and incorporating speaker-aware mechanisms or boundary detection to enhance the accuracy of speaker segmentation and ordering. This approach offers a streamlined architecture for multi-speaker ASR, potentially reducing computational costs and improving accuracy compared to traditional methods, particularly in challenging scenarios like overlapping speech and real-world conversational settings.