Kangyuan Shu

Yashesh Gaur, Nick Kibre, Jian Xue et al.

Automatic Speech Recognition (ASR) systems typically yield output in lexical form. However, humans prefer a written form output. To bridge this gap, ASR systems usually employ Inverse Text Normalization (ITN). In previous works, Weighted Finite State Transducers (WFST) have been employed to do ITN. WFSTs are nicely suited to this task but their size and run-time costs can make deployment on embedded applications challenging. In this paper, we describe the development of an on-device ITN system that is streaming, lightweight & accurate. At the core of our system is a streaming transformer tagger, that tags lexical tokens from ASR. The tag informs which ITN category might be applied, if at all. Following that, we apply an ITN-category-specific WFST, only on the tagged text, to reliably perform the ITN conversion. We show that the proposed ITN solution performs equivalent to strong baselines, while being significantly smaller in size and retaining customization capabilities.

Linxiao Zeng, Haoyun Deng, Kangyuan Shu et al.

Large Language Models (LLMs) have recently demonstrated impressive capabilities in various text generation tasks. However, it remains challenging to use them off-the-shelf in streaming applications (such as live translation), where the output must continually update as the input context expands, while still maintaining a reasonable computational cost to meet the latency requirement. In this work, we reexamine the re-translation approach to simultaneous translation and propose Self-Speculative Biased Decoding, a novel inference paradigm designed to avoid repeatedly generating output from scratch for a consistently growing input stream. We propose using the most recent output as a draft for the current growing input context. During the verification stage, the output will be biased towards the draft token for a higher draft acceptance rate. This strategy not only minimizes flickering that might distract users but also leads to higher speedups. Conventional decoding may take charge from the point of divergence after draft verification and continue until the end condition is met. Unlike existing speculative decoding strategies, our approach eliminates the need for draft computations, making it a model-agnostic and plug-and-play solution for accelerating latency-sensitive streaming applications. Experimental results on simultaneous text-to-text re-translation demonstrate that our approach achieves up to 1.7x speedup compared to conventional auto-regressive re-translation without compromising quality. Additionally, it significantly reduces flickering by 80% by incorporating the display-only mask-k technique.