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However, existing ASR models often exhibit high Word Error Rates (WER) on user-recited verses and lack full coverage of the Quranic corpus. This paper presents a systematic empirical study of domain-specific fine-tuning of pretrained Transformer-based models for Quranic ASR, using advanced speech feature extraction methods: Wav2Vec2.0, HuBERT, and XLS-R. These models apply self-supervised learning by masking portions of input audio and using Transformer architectures to learn context-aware speech features. The pretrained models are fine-tuned on a filtered Quranic dataset exceeding 870 hours of professional and user recitations. Through comprehensive ablation studies across feature extractors, output label formats, training strategies, and clip durations, we identify the","title":"A Comparative Study of Pretrained Transformer Models for Quranic ASR: Speech Representations, Label Formats, and Dataset Composition","url":"https://arxiv.org/abs/2606.19747","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.19747v1 Announce Type: new \nAbstract: Quran Automatic Speech Recognition (ASR) aims to convert Quranic recitation into text, enabling applications such as aided memorisation tools and Quranic search engines. However, existing ASR models often exhibit high Word Error Rates (WER) on user-recited verses and lack full coverage of the Quranic corpus. This paper presents a systematic empirical study of domain-specific fine-tuning of pretrained Transformer-based models for Quranic ASR, using advanced speech feature extraction methods: Wav2Vec2.0, HuBERT, and XLS-R. 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Through comprehensive ablation studies across feature extractors, output label formats, training strategies, and clip durations, we identify the","title":"A Comparative Study of Pretrained Transformer Models for Quranic ASR: Speech Representations, Label Formats, and Dataset Composition","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-06-19T04:43:39Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2606.19747"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:e4ce30826193f2fc42f5277a8902a5fdbfd95db8a29f9439e2c3ca3a1634ed52e7bbd63f6af53d2e3c54c9048e1dbb197092e669a2b5bf3b02f6d5af1d462f0c","signer":"crovia.substrate","subject":{"observed_at":"2026-06-19T04:43:39Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2606.19747"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"7706eb97ffdee89d24882038a20822059a7ce9a6d36fb7acc9b77ff84c305057","leaf_index":235477,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"764d04e5401c97cfa473e016ce89923d24fe02ce132c3da2f9d5155c4a2ec88a","side":"left"},{"sibling":"c3934d045bc174c57452095b77ba7919bc5e1a8c9a6940f5103c8d2ab16d6843","side":"right"},{"sibling":"95cd465f1abd90f3e96637c8bdcea4c7086a1e44118fbe91d0002354ae49eb77","side":"left"},{"sibling":"5383d0817bd3fca13bef9f605316cbf64ecbe708f0dcf7a96b4810183f97cd12","side":"right"},{"sibling":"2508e3a114bb5ba4b2103300fcba8bdadc88708dcf642db0fcf3d64968d4d072","side":"left"},{"sibling":"bcc64a96b2351ef6571992656cd86c436090f98a58edd222113a9eab3bbe6e32","side":"right"},{"sibling":"696ac6bcf68966ee959b35539f0bed60b3216ccb53620defd29feb8dbcb2ac30","side":"left"},{"sibling":"95ba45f2fea2d822bc863962b7e478ea50086827d90fab88c0da065d454d7ac5","side":"left"},{"sibling":"00f27f149ddd2eb0d2cf60eaed9e1d55c662cf1cf69c95fbea03bba9d35f90c7","side":"left"},{"sibling":"797e0feb6bf826a55956c876711cd24824818c5a84a591f8cc06b95577b3405d","side":"left"},{"sibling":"f049d6e86b410f6f63921a6e3aa684efffa398fd23eed28245c43b156d404c5f","side":"left"},{"sibling":"9ec7f4f84de7e2057a02ac55686567b21acfd5beaecc7a84e9e48f3db17296c2","side":"right"},{"sibling":"410c633928fea11c5b4bdddb431956b1d7c320db9cda00d2fe32e0fcf888d7b7","side":"left"},{"sibling":"b52a771530dd1686bca49e42088898b86da94879579cd6a995c6ab0598a665fe","side":"right"},{"sibling":"a116bb92f9b0350491155b470acc86d006c33ec558759e49e56614a54c39f242","side":"right"},{"sibling":"a04392fb9f2a3a620840e3b3fecd93d12e6d224e481c162389d8ae64e7194599","side":"left"},{"sibling":"c300cf0154c136afc09b1702a0be98f4ba5b6dc5cf57e8cc714ec1eaf4196eff","side":"left"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":241122,"merkle_root":"7a906c6a26ff6c6feabc2feaba6a1a70c515e6fd72a38c779293b0f78ff291c4","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260622T183701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-06-23T06:25:25Z","sig_algorithm":"ed25519","signature":"5576b1d56d5dbb0d96c780fa3ca0940d805c8de95c6251bc87297f0be058aa5e37eb53a6aa1b601381f489f093842cf674b28737ed8e46ce3a49814b5e57290c","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_cb5ca7120279bbf29e90b3a6b0ad1ca65c8d28c44c5ac1bf8e503d1bb062838c"}}