On the Role of Conversational Timing in Synthetic Training Data for ASR
Synthetic multi-speaker conversations are widely used to train conversational automatic speech recognition (ASR) systems, but it remains unclear which timing properties make simulated data most useful. This paper studies conversational timing as a controllable training variable rather than merely as a corpus statistic to be reproduced. We parameterize pause and overlap timing distributions with an exponential-tilting family estimated from multiple conversational corpora, and then explore the resulting four-dimensional parameter space with Latin hypercube sampling and multi-objective Bayesian optimization. Each sampled timing configuration is used to generate simulated training conversatio...
| Authors | Máté Gedeon, Péter Mihajlik |
|---|---|
| Primary category | eess.AS |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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