Omkar2118/datasets huggingface dataset:
ReMASC

We introduce a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems. In contrast to prior efforts, the proposed database contains genuine and replayed recordings of voice commands obtained in realistic usage scenarios and using state-of-the-art 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 database that has been specifically designed for the protection of voice controlled systems (VCS) against various forms of replay attacks.

Introducing paper: ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems
CLUES (Classifier Learning Using natural language ExplanationS)

**CLUES** is a benchmark for Classifier Learning Using natural language ExplanationS, consisting of a range of classification tasks over structured data along with natural language supervision in the form of explanations. CLUES consists of 36 real-world (CLUES-Real) and 144 synthetic (CLUES-Synthetic) classification tasks. It contains crowdsourced explanations describing real-world tasks from multiple teachers and programmatically generated explanations for the synthetic tasks.

The dataset has been created by a team of NLP researchers at UNC Chapel Hill.

Description from: [CLUES](https://clues-benchmark.github.io/)

Introducing paper: CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations