Context-Aware Dynamic Chunking for Streaming Tibetan Speech Recognition

The recent development of a streaming speech recognition framework for Amdo Tibetan marks a significant advancement in language technology. By employing a hybrid CTC/Attention architecture combined with a context-aware dynamic chunking mechanism, the framework adapts chunk widths based on encoding states, which allows for flexible information exchange and better handling of varying speaking rates. This innovation addresses the context truncation problem associated with fixed-chunk methods, leading to a notable reduction in recognition latency and maintaining performance close to global decoding standards. The framework's experimental results demonstrate a word error rate of 6.23%, representing a 48.15% relative improvement over traditional fixed-chunk approaches. This progress is vital for enhancing the recognition and processing of Tibetan speech, ultimately contributing to the broader goal of improving accessibility and usability of technology for Tibetan speakers.