MANGO: A Corpus of Human Ratings for Speech

MANGO (MUSHRA Assessment corpus using Native listeners and Guidelines to understand human Opinions at scale) is the first large-scale dataset designed for evaluating Text-to-Speech (TTS) systems in Indian languages.

Key Features:

• 47,100 human ratings of TTS-generated and ground-truth human speech.
• Covers two major Indian languages:
  • Hindi
  • Tamil
• Based on the MUSHRA (Multiple Stimuli with Hidden Reference and Anchor) test methodology.
• Ratings are provided on a continuous scale from 0 to 100, with discrete quality categories:
  • 100-80: Excellent
  • 80-60: Good
  • 60-40: Fair
  • 40-20: Poor
  • 20-0: Bad
• Includes evaluations involving:
  • Explicit (mentioned) high-quality references.
  • Hidden references to calibrate judgments.
  • Anchors for benchmarking.

Why Use MANGO?

• Addresses limitations of traditional MOS and CMOS tests.
• Enables robust benchmarking for:
  • Comparative analysis across multiple TTS systems.
  • Evaluations in diverse linguistic contexts.
  • Large-scale studies with multiple raters.

This dataset is a valuable resource for researchers and practitioners working on speech synthesis, TTS evaluation, and related fields.