Correlation Between Representation Quality of Semi-Supervised Models and Downstream Word Error Rate in Low-Resource Languages

Abstract

Abstract: Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenarios. It is likewise difficult to apply this to dialects and languages for which only limited labeled data is available. Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Such methods have shown success in natural language processing and computer vision domains, achieving new levels of performance while reducing the number of labels

Research Question

How does the representation quality of semi-supervised pre-trained models correlate with downstream word error rate reductions in low-resource language scenarios?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

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Provenance