# Defining maximum acceptable latency of AI-enhanced CAI tools

arXiv: https://arxiv.org/abs/2201.02792 · HTML: https://mlsanity.com/paper/2201.02792/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 8 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation](https://mlsanity.com/paper/1904.00930/) (2019)

## Built on by (most cited first, 4 of 4)

- [AlignAtt: Using Attention-based Audio-Translation Alignments as a Guide for Simultaneous Speech Translation](https://mlsanity.com/paper/2305.11408/) (2023)
- [Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments](https://mlsanity.com/paper/2307.03354/) (2023)
- [A Multimodal GUI Architecture for Interfacing with LLM-Based Conversational Assistants](https://mlsanity.com/paper/2510.06223/) (2025)
- [Deep Learning Model Deployment in Multiple Cloud Providers: an Exploratory Study Using Low Computing Power Environments](https://mlsanity.com/paper/2503.23988/) (2025)

## Abstract (arXiv)

Recent years have seen an increasing number of studies around the design of computer-assisted interpreting tools with integrated automatic speech processing and their use by trainees and professional interpreters. This paper discusses the role of system latency of such tools and presents the results of an experiment designed to investigate the maximum system latency that is cognitively acceptable for interpreters working in the simultaneous modality. The results show that interpreters can cope with a system latency of 3 seconds without any major impact in the rendition of the original text, both in terms of accuracy and fluency. This value is above the typical latency of available AI-based CAI tools and paves the way to experiment with larger context-based language models and higher latencies.