Seeds Before Objectives: Rethinking Evaluation for Low-Resource Garhwali ASR
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Computer Science > Computation and Language
Title:Seeds Before Objectives: Rethinking Evaluation for Low-Resource Garhwali ASR
Abstract:At corpus sizes typical of low-resource dialects, single-run comparisons can yield gains that do not replicate. We show this for Garhwali, an under-resourced Indo-Aryan language of the central Himalaya, building the first reproducible multi-seed ASR benchmark on the official VAANI splits, with per-seed outputs and significance testing. Re-examining plausible gains, we find them fragile: neither Focal CTC nor a matra-weighted objective beats standard CTC under seed-level testing, the matra objective fails to cut even its targeted errors, and Hindi-to-Garhwali transfer gives no gain over direct fine-tuning. What holds up is mundane: w2v-BERT 2.0 with standard CTC reaches 47.0% WER over five seeds, beating the larger MMS-1B and comparable models; pretraining design, not parameter count, drives performance, and speed augmentation gives a small, largely consistent gain. Multi-seed evaluation on official splits separates real gains from seed noise.
| Comments: | 19 pages, 3 figures. Accepted for oral presentation at ICNLSP 2026, Trento, Italy, September 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.10670 [cs.CL] |
| (or arXiv:2608.10670v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.10670
arXiv-issued DOI via DataCite (pending registration)
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