Orukeet: Multilingual ASR with Frozen Gabor Kernels

Nathan Roll; Irene Yi; Büşra Marşan; Vianney Grenez; Gabriel Stein; Momcilo Mrkaic; Pavle Padjin; Vladimir Zeljkovic; Calbert Graham

arXiv · 2609.10054

Orukeet: Multilingual ASR with Frozen Gabor Kernels

Nathan Roll·Irene Yi·Büşra Marşan·Vianney Grenez·Gabriel Stein·Momcilo Mrkaic·Pavle Padjin·Vladimir Zeljkovic·Calbert Graham

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Abstract

Orukeet replaces half of an adapted Parakeet encoder's temporal filters with 12,288 fitted Gabor kernels, freezes these replacements, and trains the remaining parameters on multilingual and multi-accent data. Final adaptation and checkpoint selection use LibriSpeech test-other. Across 20,146 FLEURS recordings in 25 languages, pooled word error rate (WER) falls from Parakeet's 11.01% to Orukeet's 9.85%, a 10.6% relative reduction. Orukeet has lower WER on 23 of the 25 languages. Orukeet outperforms Parakeet on 61 out of 74 tested splits, including LibriSpeech test-clean (1.46% vs. 1.53% WER), test-other (2.86% vs. 3.14%), and FLEURS English (3.82% vs. 4.28%). All comparisons decode the same audio with matched NeMo settings. The fitted kernels are stored as ordinary convolution weights, retaining Parakeet's architecture and inference operators.

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BibTeX RIS

Nathan Roll, Irene Yi, Büşra Marşan, Vianney Grenez, Gabriel Stein, Momcilo Mrkaic, Pavle Padjin, Vladimir Zeljkovic, Calbert Graham. 2026-09-09. Orukeet: Multilingual ASR with Frozen Gabor Kernels. https://arxiv.org/abs/2609.10054

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