{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4KAOICC45CRI3WK4RX7PFURO3L","short_pith_number":"pith:4KAOICC4","schema_version":"1.0","canonical_sha256":"e280e4085ce8a28dd95c8dfef2d22edaf40af9cfdf976b6e390d8a10c7019958","source":{"kind":"arxiv","id":"2607.02763","version":1},"attestation_state":"computed","paper":{"title":"LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alessio Brutti, Marco Matassoni, Nina Hosseini-Kivanani","submitted_at":"2026-07-02T21:00:52Z","abstract_excerpt":"Spoken Question Answering (SQA) remains largely focused on high-resource languages and carefully recorded speech, limiting the reach of speech-LLM methods in low-resource settings. This paper investigates whether text-to-speech (TTS) can provide task-specific training data for Luxembourgish SQA without requiring a large human-recorded QA corpus. Starting from existing text-based QA resources, we translate questions into Luxembourgish, synthesize spoken questions with multiple TTS systems, and pair them with textual answers. We train a parameter-efficient SLAM-style architecture that connects a"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.02763","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-02T21:00:52Z","cross_cats_sorted":[],"title_canon_sha256":"356fbeaa335ca4bfa6ce9a602ffa05d3a68009d572b91964b504b48eb9e68589","abstract_canon_sha256":"3b432fdf25c520e6716546203b891521a3450d7f694ba70f1c2668221a310edf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T00:16:12.611019Z","signature_b64":"RNtWOqMJz+9BdSpCZtdznCJc2WQpZhN8HukCOEzHLJkqffUCiPW056i+RRzlkqWxyshfYEcMWW0h+2lG44L4BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e280e4085ce8a28dd95c8dfef2d22edaf40af9cfdf976b6e390d8a10c7019958","last_reissued_at":"2026-07-07T00:16:12.610256Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T00:16:12.610256Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alessio Brutti, Marco Matassoni, Nina Hosseini-Kivanani","submitted_at":"2026-07-02T21:00:52Z","abstract_excerpt":"Spoken Question Answering (SQA) remains largely focused on high-resource languages and carefully recorded speech, limiting the reach of speech-LLM methods in low-resource settings. This paper investigates whether text-to-speech (TTS) can provide task-specific training data for Luxembourgish SQA without requiring a large human-recorded QA corpus. Starting from existing text-based QA resources, we translate questions into Luxembourgish, synthesize spoken questions with multiple TTS systems, and pair them with textual answers. We train a parameter-efficient SLAM-style architecture that connects a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.02763","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.02763/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.02763","created_at":"2026-07-07T00:16:12.610384+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.02763v1","created_at":"2026-07-07T00:16:12.610384+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.02763","created_at":"2026-07-07T00:16:12.610384+00:00"},{"alias_kind":"pith_short_12","alias_value":"4KAOICC45CRI","created_at":"2026-07-07T00:16:12.610384+00:00"},{"alias_kind":"pith_short_16","alias_value":"4KAOICC45CRI3WK4","created_at":"2026-07-07T00:16:12.610384+00:00"},{"alias_kind":"pith_short_8","alias_value":"4KAOICC4","created_at":"2026-07-07T00:16:12.610384+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4KAOICC45CRI3WK4RX7PFURO3L","json":"https://pith.science/pith/4KAOICC45CRI3WK4RX7PFURO3L.json","graph_json":"https://pith.science/api/pith-number/4KAOICC45CRI3WK4RX7PFURO3L/graph.json","events_json":"https://pith.science/api/pith-number/4KAOICC45CRI3WK4RX7PFURO3L/events.json","paper":"https://pith.science/paper/4KAOICC4"},"agent_actions":{"view_html":"https://pith.science/pith/4KAOICC45CRI3WK4RX7PFURO3L","download_json":"https://pith.science/pith/4KAOICC45CRI3WK4RX7PFURO3L.json","view_paper":"https://pith.science/paper/4KAOICC4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.02763&json=true","fetch_graph":"https://pith.science/api/pith-number/4KAOICC45CRI3WK4RX7PFURO3L/graph.json","fetch_events":"https://pith.science/api/pith-number/4KAOICC45CRI3WK4RX7PFURO3L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4KAOICC45CRI3WK4RX7PFURO3L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4KAOICC45CRI3WK4RX7PFURO3L/action/storage_attestation","attest_author":"https://pith.science/pith/4KAOICC45CRI3WK4RX7PFURO3L/action/author_attestation","sign_citation":"https://pith.science/pith/4KAOICC45CRI3WK4RX7PFURO3L/action/citation_signature","submit_replication":"https://pith.science/pith/4KAOICC45CRI3WK4RX7PFURO3L/action/replication_record"}},"created_at":"2026-07-07T00:16:12.610384+00:00","updated_at":"2026-07-07T00:16:12.610384+00:00"}