{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:K7C7CMBSMSNHQFZXDX224JZBYT","short_pith_number":"pith:K7C7CMBS","schema_version":"1.0","canonical_sha256":"57c5f13032649a7817371df5ae2721c4e00b8b242317d2d65b9516b105d83b44","source":{"kind":"arxiv","id":"2304.13121","version":2},"attestation_state":"computed","paper":{"title":"Multi-Speaker Multi-Lingual VQTTS System for LIMMITS 2023 Challenge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Chenpeng Du, Feiyu Shen, Kai Yu, Yiwei Guo","submitted_at":"2023-04-25T19:54:37Z","abstract_excerpt":"In this paper, we describe the systems developed by the SJTU X-LANCE team for LIMMITS 2023 Challenge, and we mainly focus on the winning system on naturalness for track 1. The aim of this challenge is to build a multi-speaker multi-lingual text-to-speech (TTS) system for Marathi, Hindi and Telugu. Each of the languages has a male and a female speaker in the given dataset. In track 1, only 5 hours data from each speaker can be selected to train the TTS model. Our system is based on the recently proposed VQTTS that utilizes VQ acoustic feature rather than mel-spectrogram. We introduce additional"},"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":"2304.13121","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-04-25T19:54:37Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"c0c432b7f6aeb5a0da5f865bc6eabaad0a4a56d8aedc1c10ba07331376cf542d","abstract_canon_sha256":"0a460bfaa4e7b56ce6969ba85b65992b14b33bc57b8e71865ddca9ae66712287"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:33:07.325339Z","signature_b64":"O2gfr3wj1LFJon86g4H6gFSGQMBCE22yzwSgZoPUk9mr+V4OSNqpz6+TqJRjB5rmk+eaHH+B1s9u63wXOsW4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57c5f13032649a7817371df5ae2721c4e00b8b242317d2d65b9516b105d83b44","last_reissued_at":"2026-07-05T09:33:07.324861Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:33:07.324861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Speaker Multi-Lingual VQTTS System for LIMMITS 2023 Challenge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Chenpeng Du, Feiyu Shen, Kai Yu, Yiwei Guo","submitted_at":"2023-04-25T19:54:37Z","abstract_excerpt":"In this paper, we describe the systems developed by the SJTU X-LANCE team for LIMMITS 2023 Challenge, and we mainly focus on the winning system on naturalness for track 1. The aim of this challenge is to build a multi-speaker multi-lingual text-to-speech (TTS) system for Marathi, Hindi and Telugu. Each of the languages has a male and a female speaker in the given dataset. In track 1, only 5 hours data from each speaker can be selected to train the TTS model. Our system is based on the recently proposed VQTTS that utilizes VQ acoustic feature rather than mel-spectrogram. We introduce additional"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.13121","kind":"arxiv","version":2},"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/2304.13121/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":"2304.13121","created_at":"2026-07-05T09:33:07.324922+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.13121v2","created_at":"2026-07-05T09:33:07.324922+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.13121","created_at":"2026-07-05T09:33:07.324922+00:00"},{"alias_kind":"pith_short_12","alias_value":"K7C7CMBSMSNH","created_at":"2026-07-05T09:33:07.324922+00:00"},{"alias_kind":"pith_short_16","alias_value":"K7C7CMBSMSNHQFZX","created_at":"2026-07-05T09:33:07.324922+00:00"},{"alias_kind":"pith_short_8","alias_value":"K7C7CMBS","created_at":"2026-07-05T09:33:07.324922+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/K7C7CMBSMSNHQFZXDX224JZBYT","json":"https://pith.science/pith/K7C7CMBSMSNHQFZXDX224JZBYT.json","graph_json":"https://pith.science/api/pith-number/K7C7CMBSMSNHQFZXDX224JZBYT/graph.json","events_json":"https://pith.science/api/pith-number/K7C7CMBSMSNHQFZXDX224JZBYT/events.json","paper":"https://pith.science/paper/K7C7CMBS"},"agent_actions":{"view_html":"https://pith.science/pith/K7C7CMBSMSNHQFZXDX224JZBYT","download_json":"https://pith.science/pith/K7C7CMBSMSNHQFZXDX224JZBYT.json","view_paper":"https://pith.science/paper/K7C7CMBS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.13121&json=true","fetch_graph":"https://pith.science/api/pith-number/K7C7CMBSMSNHQFZXDX224JZBYT/graph.json","fetch_events":"https://pith.science/api/pith-number/K7C7CMBSMSNHQFZXDX224JZBYT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K7C7CMBSMSNHQFZXDX224JZBYT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K7C7CMBSMSNHQFZXDX224JZBYT/action/storage_attestation","attest_author":"https://pith.science/pith/K7C7CMBSMSNHQFZXDX224JZBYT/action/author_attestation","sign_citation":"https://pith.science/pith/K7C7CMBSMSNHQFZXDX224JZBYT/action/citation_signature","submit_replication":"https://pith.science/pith/K7C7CMBSMSNHQFZXDX224JZBYT/action/replication_record"}},"created_at":"2026-07-05T09:33:07.324922+00:00","updated_at":"2026-07-05T09:33:07.324922+00:00"}