ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts

TL;DR

ShiftySpeech is a comprehensive synthetic speech dataset designed to evaluate how well detection models generalize under various realistic distribution shifts, revealing significant performance degradation in current methods.

Contribution

We introduce ShiftySpeech, a large-scale benchmark dataset that systematically covers diverse distribution shifts in synthetic speech for robust evaluation.

Findings

Distribution shifts significantly reduce detection accuracy.

Current state-of-the-art methods are vulnerable to distribution shifts.

Benchmark enables detailed analysis of model robustness.

Abstract

The problem of synthetic speech detection has enjoyed considerable attention, with recent methods achieving low error rates across several established benchmarks. However, to what extent can low error rates on academic benchmarks translate to more realistic conditions? In practice, while the training set is fixed at one point in time, test-time conditions may exhibit distribution shifts relative to the training conditions, such as changes in speaker characteristics, emotional expressiveness, language and acoustic conditions, and the emergence of novel synthesis methods. Although some existing datasets target subsets of these distribution shifts, systematic analysis remains difficult due to inconsistencies between source data and synthesis systems across datasets. This difficulty is further exacerbated by the rapid development of new text-to-speech (TTS) and vocoder systems, which continually expand the diversity of synthetic speech. To enable systematic benchmarking of model performance under distribution shifts, we introduce ShiftySpeech, a large-scale benchmark comprising over 3,000 hours of synthetic speech across 7 source domains, 6 TTS systems, 12 vocoders, and 3 languages. ShiftySpeech is specifically designed to evaluate model generalization under controlled distribution shifts while ensuring broad coverage of modern synthetic speech generation techniques. It fills a key gap in current benchmarks by supporting fine-grained, controlled analysis of generalization robustness. All tested distribution shifts significantly degrade detection performance of state-of-the-art detection approaches based on self-supervised features. Overall, our findings suggest that reliance on synthetic speech detection methods in production environments should be carefully evaluated based on anticipated distribution shifts.