Crowdsourced Multilingual Speech Intelligibility Testing

Abstract

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.

References in corpus (5)

• Speech Enhancement and Dereverberation with Diffusion-based Generative Models
• StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation
• An Open source Implementation of ITU-T Recommendation P.808 with Validation
• Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence
• Effective data screening technique for crowdsourced speech intelligibility experiments: Evaluation with IRM-based speech enhancement