SYNTHETIC AUDIO DATASETS FOR EVALUATING CONVERSATIONAL AI SYSTEMS

Abstract

This article examines the evolving landscape of conversational AI evaluation through synthetic audio datasets. Traditional evaluation methods relying on human-graded interactions face significant limitations in scalability, coverage, and resource efficiency, creating a bottleneck in the development pipeline for voice-based systems. The article explores how synthetic datasets generated through text-to-speech systems and scripted dialogue generation offer promising alternatives by enabling systematic coverage of diverse interaction patterns, including rare edge cases that often reveal critical system limitations. The article encompasses the approaches to synthetic data generation, highlighting how modern neural TTS technologies and sophisticated dialogue simulation frameworks can create realistic conversational corpora with controllable parameters. The benefits of synthetic datasets are analyzed, including enhanced coverage, scalability, and automatic quality labeling capabilities. Implementation considerations focus on balancing realism with systematic exploration, while acknowledging the remaining challenges in bridging the authenticity gap between synthetic and real conversations. We conclude by examining the future trajectory of hybrid evaluation methodologies that strategically combine synthetic and real-world data throughout the development lifecycle.