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

2026-07-09 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A study investigates the impact of conversational timing properties on synthetic training data for automatic speech recognition (ASR) systems. Researchers parameterized pause and overlap timing distributions using an exponential-tilting family, exploring a four-dimensional space with Latin hypercube sampling and multi-objective Bayesian optimization. Each timing configuration generated simulated conversations, trained an ASR system, and was evaluated on a Hungarian dialogue corpus using concatenated-permutation word and character error rates (cpWER and cpCER). Results indicate that higher overlap exposure correlates with lower cpWER, while longer, more variable gaps increase cpWER. Bayesian optimization revealed an overlap-gap trade-off, suggesting that realistic simulation needs task-relevant diagnostics of timing profiles.

Key takeaway

For ASR system developers optimizing synthetic training data, prioritize higher overlap exposure and controlled, shorter gaps. Your focus should shift from merely reproducing corpus statistics to actively diagnosing and tuning timing profiles, particularly to manage the overlap-gap trade-off for improved concatenated-permutation word error rates. This approach will enhance the effectiveness of your simulated conversational training data.

Key insights

Conversational timing, specifically overlap and gap characteristics, significantly impacts ASR performance in synthetic training data.

Principles

• Higher overlap exposure improves ASR performance.
• Longer, more variable gaps degrade ASR performance.
• ASR behavior correlates with induced timing statistics.

Method

Parameterize pause/overlap timing with exponential-tilting, explore space via Latin hypercube sampling and multi-objective Bayesian optimization, then generate data and train ASR.

In practice

• Complement simulation with overlap, gap, and timing-variability diagnostics.
• Actively tune timing profiles beyond corpus reproduction.

Topics

• Automatic Speech Recognition
• Synthetic Data
• Conversational Timing
• Bayesian Optimization
• Word Error Rate
• Dialogue Systems

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.