CUSIDE-T: Chunking, Simulating Future and Decoding for Transducer based Streaming ASR
<|reference_start|>CUSIDE-T: Chunking, Simulating Future and Decoding for Transducer based Streaming ASR: Streaming automatic speech recognition (ASR) is very important for many real-world ASR applications. However, a notable challenge for streaming ASR systems lies in balancing operational performance against latency constraint. Recently, a method of chunking, simulating future context and decoding, called CUSIDE, has been proposed for connectionist temporal classification (CTC) based streaming ASR, which obtains a good balance between reduced latency and high recognition accuracy. In this paper, we present CUSIDE-T, which successfully adapts the CUSIDE method over the recurrent neural network transducer (RNN-T) ASR architecture, instead of being based on the CTC architecture. We also incorporate language model rescoring in CUSIDE-T to further enhance accuracy, while only bringing a small additional latency. Extensive experiments are conducted over the AISHELL-1, WenetSpeech and SpeechIO datasets, comparing CUSIDE-T and U2++ (both based on RNN-T). U2++ is an existing counterpart of chunk based streaming ASR method. It is shown that CUSIDE-T achieves superior accuracy performance for streaming ASR, with equal settings of latency.<|reference_end|>
@article{zhao2024cuside-t:,
    title={CUSIDE-T: Chunking, Simulating Future and Decoding for Transducer based
  Streaming ASR},
    author={Wenbo Zhao, Ziwei Li, Chuan Yu, Zhijian Ou},
    journal={arXiv preprint arXiv:2407.10255},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10255},
    primaryClass={cs.SD eess.AS}
}