arXiv:2607.08371v1 Announce Type: cross Abstract: 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
The increasing reliance on synthetic data for training complex AI systems like ASR necessitates deeper understanding of optimal generation parameters, a critical need as AI adoption accelerates.
Improving synthetic data generation for ASR directly enhances the efficiency and performance of conversational AI, impacting numerous applications and user experiences.
This research provides a methodology to optimize synthetic training data for ASR by systemically analyzing conversational timing, potentially leading to more robust and accurate speech recognition systems.
- · AI developers
- · ASR system providers
- · Conversational AI companies
- · Cloud service providers
- · ASR systems with suboptimal training data
- · Companies relying on inefficient data generation methods
ASR systems become more accurate and less resource-intensive to train due to optimized synthetic data.
Improved conversational AI leads to broader adoption across industries, automating more human-computer interactions.
The methodology for optimizing synthetic data generation extends to other AI domains, accelerating overall AI development and deployment.
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Read at arXiv cs.AI