Decoder-only language models (LMs) have been successfully adopted for
speech-processing tasks including automatic speech recognition (ASR). The LMs
have ample expressiveness and perform efficiently. This efficiency is a
suitable characteristic for streaming applications of ASR. In this work, we
propose to use a decoder-only architecture for blockwise streaming ASR. In our
approach, speech features are compressed using CTC output and context embedding
using blockwise speech subnetwork, and are sequentially provided as prompts to
the decoder. The decoder estimates the output tokens promptly at each block. To
this end, we also propose a novel training scheme using random-length prefix
prompts to make the model robust to the truncated prompts caused by blockwise
processing. An experimental comparison shows that our proposed decoder-only
streaming ASR achieves 8% relative word error rate reduction in the LibriSpeech
test-other set while being twice as fast as the baseline model.

使用解码器专用架构进行分块流式自动语音识别，通过压缩 CTC 输出和上下文嵌入的语音特征，并将其作为提示逐块顺序提供给解码器，以快速估计输出令牌，同时采用随机长度前缀提示的新型训练方案，使模型能够应对分块处理引起的截断提示，并且在 LibriSpeech test-other 数据集上相对词错误率减少了 8%，速度是基线模型的两倍。