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What does it take to get state of the art in simultaneous speech-to-speech translation?

Vincent Wilmet, Johnson Du

TL;DR

An in-depth analysis of the latency characteristics observed in simultaneous speech-to-speech model's performance, particularly focusing on hallucination-induced latency spikes, suggests that a combination of careful input management and strategic parameter adjustments can significantly enhance speech-to-speech model's latency behavior.

Abstract

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.

What does it take to get state of the art in simultaneous speech-to-speech translation?

TL;DR

An in-depth analysis of the latency characteristics observed in simultaneous speech-to-speech model's performance, particularly focusing on hallucination-induced latency spikes, suggests that a combination of careful input management and strategic parameter adjustments can significantly enhance speech-to-speech model's latency behavior.

Abstract

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.
Paper Structure (29 sections, 16 equations, 4 figures, 2 tables)

This paper contains 29 sections, 16 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Observations
  3. Input Behavior
  4. Hallucination Patterns
  5. Latency Spikes in Non-Hallucinated Outputs
  6. Findings
  7. Minimizing Latency Through Hallucination Control
  8. Methodology
  9. Threshold and Parameter Adjustments
  10. Lookback Strategy
  11. Evaluation Metrics and Strategies
  12. Average Lagging (AL)
  13. Differentiable Average Lagging (DAL)
  14. Average Proportion (AP)
  15. Average Target Delay (ATD)
  16. ...and 14 more sections

Figures (4)

  • Figure 1: ASR Latency vs WER. This figure shows the relationship between ASR latency and Word Error Rate (WER) for different model sizes.
  • Figure 2: Proper Noun Accuracy vs Average Lagging Tradeoff (median)
  • Figure 3: ASR Latency vs BLEU Score (Averaged Data). This figure shows the relationship between ASR latency and BLEU score for different model sizes (small, medium, large-v2).
  • Figure 4: Impact of Glossary Prefix on ASR Performance. This figure illustrates how incorporating a glossary prefix into the ASR module improves the model's ability to accurately transcribe proper nouns and domain-specific terms. The data points show a marked improvement in median transcription accuracy when the glossary prefix is used.