Voice dictation is an increasingly important text input modality. Existing
systems that allow both dictation and editing-by-voice restrict their command
language to flat templates invoked by trigger words. In this work, we study the
feasibility of allowing users to interrupt their dictation with spoken editing
commands in open-ended natural language. We introduce a new task and dataset,
TERTiUS, to experiment with such systems. To support this flexibility in
real-time, a system must incrementally segment and classify spans of speech as
either dictation or command, and interpret the spans that are commands. We
experiment with using large pre-trained language models to predict the edited
text, or alternatively, to predict a small text-editing program. Experiments
show a natural trade-off between model accuracy and latency: a smaller model
achieves 30% end-state accuracy with 1.3 seconds of latency, while a larger
model achieves 55% end-state accuracy with 7 seconds of latency.

语音输入越来越重要。我们研究了允许用户使用开放式自然语言中断他们的语音输入以实现编辑的可行性。我们引入了一个新的任务和数据集，TERTiUS，来实验这样的系统。通过使用大型预训练语言模型预测编辑后的文本或预测一个小型文本编辑程序，我们展示了模型准确性和延迟之间的自然权衡。