Transformer-based language modeling and decoding for conversational speech recognition
Columbia University in the City of New York（纽约哥伦比亚大学）
英文摘要：
We propose a way to use a transformer-based language model in conversational speech recognition. Specifically, we focus on decoding efficiently in a weighted finite-state transducer framework. We showcase an approach to lattice re-scoring that allows for longer range history captured by a transfomer-based language model and takes advantage of a transformer's ability to avoid computing sequentially.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
