An offline-trained speech LLM with boundary-aware CIF speech prompts and test-time wait-k decoding achieves better quality-latency trade-offs in simultaneous speech-to-speech translation than StreamSpeech on CVSS-C.
Average Token Delay: A Latency Metric for Simultaneous Translation
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Simultaneous translation is a task in which translation begins before the speaker has finished speaking. In its evaluation, we have to consider the latency of the translation in addition to the quality. The latency is preferably as small as possible for users to comprehend what the speaker says with a small delay. Existing latency metrics focus on when the translation starts but do not consider adequately when the translation ends. This means such metrics do not penalize the latency caused by a long translation output, which actually delays users' comprehension. In this work, we propose a novel latency evaluation metric called Average Token Delay (ATD) that focuses on the end timings of partial translations in simultaneous translation. We discuss the advantage of ATD using simulated examples and also investigate the differences between ATD and Average Lagging with simultaneous translation experiments.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
SimulS2S-LLM: Unlocking Simultaneous Inference of Speech LLMs for Speech-to-Speech Translation
An offline-trained speech LLM with boundary-aware CIF speech prompts and test-time wait-k decoding achieves better quality-latency trade-offs in simultaneous speech-to-speech translation than StreamSpeech on CVSS-C.