Does incorporating explicit phoneme alignment into UniSpeech-derived representations improve robustness in low

Abstract

Abstract: Domain-specific languages that use a lot of specific terminology often fall into the category of low-resource languages. Collecting test datasets in a narrow domain is time-consuming and requires skilled human resources with domain knowledge and training for the annotation task. This study addresses the challenge of automated collecting test datasets to evaluate semantic search in low-resource domain-specific German language of the process industry. Our approach proposes an end-to-end annotation pipeline for automated query generation to the score reassessment of query-document pairs. To overc

Research Question

Does incorporating explicit phoneme alignment into UniSpeech-derived representations improve robustness in low-resource languages on LibriSpeech and CommonVoice benchmarks compared to baseline self-supervised models?

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Quality Dimensions

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

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance