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This research introduces Orukeet, an automatic speech recognition model that integrates frozen Gabor kernels into a Parakeet encoder to significantly improve multilingual accuracy. By replacing half of the temporal filters with these fitted kernels, the approach achieves a 10.6% relative reduction in word error rate across 25 languages while maintaining standard inference operators. The study demonstrates that architectural modifications focused on specific frequency-domain features can yield substantial performance gains without altering the underlying model's deployment requirements.
Orukeet: Multilingual ASR with Frozen Gabor Kernels
arXiv:2609.10054v1 Announce Type: cross 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.
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.
5 pages, 2 figures. Code and model: this https URL
Sound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
arXiv:2609.10054 [cs.SD]
(or arXiv:2609.10054v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2609.10054 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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