# Crowdsourced Multilingual Speech Intelligibility Testing

arXiv: https://arxiv.org/abs/2403.14817 · HTML: https://mlsanity.com/paper/2403.14817/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2212.11851/) (2022)
- [ICASSP 2023 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2303.11510/) (2023)
- [An Open source Implementation of ITU-T Recommendation P.808 with Validation](https://mlsanity.com/paper/2005.08138/) (2020)
- [Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence](https://mlsanity.com/paper/2110.11438/) (2021)
- [Speech Quality Assessment in Crowdsourcing: Comparison Category Rating Method](https://mlsanity.com/paper/2104.04371/) (2021)
- [Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model](https://mlsanity.com/paper/2205.05785/) (2022)
- [Comparison of remote experiments using crowdsourcing and laboratory experiments on speech intelligibility](https://mlsanity.com/paper/2104.10001/) (2021)
- [Effective data screening technique for crowdsourced speech intelligibility experiments: Evaluation with IRM-based speech enhancement](https://mlsanity.com/paper/2203.16760/) (2022)
- [On Crowdsourcing-design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms](https://mlsanity.com/paper/2306.01538/) (2023)

## Abstract (arXiv)

With the advent of generative audio features, there is an increasing need for rapid evaluation of their impact on speech intelligibility. Beyond the existing laboratory measures, which are expensive and do not scale well, there has been comparatively little work on crowdsourced assessment of intelligibility. Standards and recommendations are yet to be defined, and publicly available multilingual test materials are lacking. In response to this challenge, we propose an approach for a crowdsourced intelligibility assessment. We detail the test design, the collection and public release of the multilingual speech data, and the results of our early experiments.