# ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems

arXiv: https://arxiv.org/abs/1904.03365 · HTML: https://mlsanity.com/paper/1904.03365/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Data Quality as Predictor of Voice Anti-Spoofing Generalization](https://mlsanity.com/paper/2103.14602/) (2021)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [Online Adversarial Attacks](https://mlsanity.com/paper/2103.02014/) (2021)
- [Alethia: A Foundational Encoder for Voice Deepfakes](https://mlsanity.com/paper/2605.00251/) (2026)
- [Practical Speech Re-use Prevention in Voice-driven Services](https://mlsanity.com/paper/2101.04773/) (2021)
- [Multi-Channel Replay Speech Detection using Acoustic Maps](https://mlsanity.com/paper/2602.16399/) (2026)
- [A survey of AI-generated voices and their detection](https://mlsanity.com/paper/2608.15411/) (2026)

## Abstract (arXiv)

This paper introduces a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voice assistant development kits. Specifically, the database contains recordings from four systems (each with a different microphone array) in a variety of environmental conditions with different forms of background noise and relative positions between speaker and device. To the best of our knowledge, this is the first publicly available database that has been specifically designed for the protection of state-of-the-art voice-controlled systems against various replay attacks in various conditions and environments.