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cs.CL2026
Phoneme- and Word-Level Metrics Using Self-Supervised Speech Representations for Forced Alignment Evaluation
V. S. D. S. Mahesh Akavarapu, Michael Daniel, Gerhard Jäger
Forced alignment evaluation typically requires manually annotated timestamps, limiting large-scale and multilingual analysis. We introduce two corpus-level metrics based on self-su…
cs.CL2026
Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages
V. S. D. S. Mahesh Akavarapu, Michael Daniel, Gerhard Jäger
We present a phoneme-level analysis of automatic speech recognition (ASR) for two low-resourced and phonologically complex East Caucasian languages, Archi and Rutul, based on curat…