What does it take to get state of the art in simultaneous speech-to-speech translation?
This addresses latency issues for real-time speech translation systems, but it is incremental as it builds on existing methods.
The paper tackled the problem of latency spikes in simultaneous speech-to-speech translation models, finding that input management and parameter adjustments can significantly improve latency behavior.
This paper presents an in-depth analysis of the latency characteristics observed in simultaneous speech-to-speech model's performance, particularly focusing on hallucination-induced latency spikes. By systematically experimenting with various input parameters and conditions, we propose methods to minimize latency spikes and improve overall performance. The findings suggest that a combination of careful input management and strategic parameter adjustments can significantly enhance speech-to-speech model's latency behavior.