On the Role of Conversational Timing in Synthetic Training Data for ASR

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

This study investigates the optimal conversational timing properties for synthetic training data used in Automatic Speech Recognition (ASR) systems. Researchers parameterized pause and overlap timing distributions using an exponential-tilting family, defining a four-dimensional parameter space. This space was explored via Latin hypercube sampling and multi-objective Bayesian optimization, with each of 25 sampled configurations generating training data for an ASR system. Evaluation on a Hungarian dialogue corpus, BEA-Dialogue, using concatenated-permutation word and character error rates (cpWER and cpCER), revealed that induced timing statistics directly explain ASR performance. Specifically, increased overlap exposure correlates with lower cpWER, while longer and more variable gaps lead to higher cpWER. The Bayesian optimization provided analytical insights into an overlap–gap trade-off, suggesting that task-relevant diagnostics of timing profiles are crucial beyond mere realism.

Key takeaway

For machine learning engineers designing synthetic training data for conversational ASR, you should actively shape timing distributions rather than merely replicating corpus statistics. Prioritize increasing overlap exposure and reducing the dominance of long, variable gaps in your simulated conversations, as this directly correlates with lower cpWER. Monitor the generated overlap rate, gap tail mass, and delay variability, as these intrinsic timing statistics are more actionable for improving ASR performance than raw simulator parameters.

Key insights

Optimizing synthetic ASR training data timing, not just realism, improves recognition performance.

Principles

• Higher overlap exposure reduces ASR word error rates.
• Longer, more variable gaps increase ASR word error rates.
• Task-relevant timing diagnostics are crucial for simulation design.

Method

Parameterize conversational timing with exponential tilting, then optimize using Latin hypercube sampling and multi-objective Bayesian optimization.

In practice

• Monitor generated overlap, gap, and variability profiles.
• Shift timing distributions to increase overlap exposure.
• Reduce long-gap dominance in synthetic training data.

Topics

• Automatic Speech Recognition
• Synthetic Data Generation
• Conversational Timing
• Bayesian Optimization
• Overlap-Gap Trade-off
• Exponential Tilting

Code references

• gedeonmate/conversation-timing

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.