{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3HA6UPTOLQKVIHHAKAG2ITQS2R","short_pith_number":"pith:3HA6UPTO","schema_version":"1.0","canonical_sha256":"d9c1ea3e6e5c15541ce0500da44e12d444f1da83abaee7614458cdd75e33799c","source":{"kind":"arxiv","id":"2201.06786","version":2},"attestation_state":"computed","paper":{"title":"Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.RO"],"primary_cat":"cs.AI","authors_text":"Akira Taniguchi, Hiroaki Murakami, Ryo Ozaki, Tadahiro Taniguchi","submitted_at":"2022-01-18T07:31:59Z","abstract_excerpt":"Human infants acquire their verbal lexicon with minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other sensory stimuli. This study proposes a novel fully unsupervised learning method for discovering speech units using phonological information as a distributional cue and object information as a co-occurrence cue. The proposed method can acquire words and phonemes from speech signals using unsupervised learning and utilize object information based on multiple modalities-vision, tactile, and auditory-simultaneously. The"},"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":"2201.06786","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2022-01-18T07:31:59Z","cross_cats_sorted":["cs.CL","cs.RO"],"title_canon_sha256":"f31e85fa0ac7c4a67458fc4de0e77fd9035a6705ca0fb31aa191f7d3175b8801","abstract_canon_sha256":"4b992cce5438cf30cf9193418ca324343042f5d276247cbcd0a9f9e9be73b833"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:42:55.641857Z","signature_b64":"vWvXNqgtzXzlMGAthYW1sNagCJWO1sZ9ReU4fMXEmd9XPAMuZD57M3dfF/kcYQpqS3Wf2kR+imkjg5TFpGWZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9c1ea3e6e5c15541ce0500da44e12d444f1da83abaee7614458cdd75e33799c","last_reissued_at":"2026-07-05T06:42:55.641390Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:42:55.641390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.RO"],"primary_cat":"cs.AI","authors_text":"Akira Taniguchi, Hiroaki Murakami, Ryo Ozaki, Tadahiro Taniguchi","submitted_at":"2022-01-18T07:31:59Z","abstract_excerpt":"Human infants acquire their verbal lexicon with minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other sensory stimuli. This study proposes a novel fully unsupervised learning method for discovering speech units using phonological information as a distributional cue and object information as a co-occurrence cue. The proposed method can acquire words and phonemes from speech signals using unsupervised learning and utilize object information based on multiple modalities-vision, tactile, and auditory-simultaneously. The"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.06786","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/2201.06786/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":"2201.06786","created_at":"2026-07-05T06:42:55.641452+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.06786v2","created_at":"2026-07-05T06:42:55.641452+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.06786","created_at":"2026-07-05T06:42:55.641452+00:00"},{"alias_kind":"pith_short_12","alias_value":"3HA6UPTOLQKV","created_at":"2026-07-05T06:42:55.641452+00:00"},{"alias_kind":"pith_short_16","alias_value":"3HA6UPTOLQKVIHHA","created_at":"2026-07-05T06:42:55.641452+00:00"},{"alias_kind":"pith_short_8","alias_value":"3HA6UPTO","created_at":"2026-07-05T06:42:55.641452+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15721","citing_title":"On Parallelism in Music and Language: A Perspective from Symbol Emergence Systems based on Probabilistic Generative Models","ref_index":58,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3HA6UPTOLQKVIHHAKAG2ITQS2R","json":"https://pith.science/pith/3HA6UPTOLQKVIHHAKAG2ITQS2R.json","graph_json":"https://pith.science/api/pith-number/3HA6UPTOLQKVIHHAKAG2ITQS2R/graph.json","events_json":"https://pith.science/api/pith-number/3HA6UPTOLQKVIHHAKAG2ITQS2R/events.json","paper":"https://pith.science/paper/3HA6UPTO"},"agent_actions":{"view_html":"https://pith.science/pith/3HA6UPTOLQKVIHHAKAG2ITQS2R","download_json":"https://pith.science/pith/3HA6UPTOLQKVIHHAKAG2ITQS2R.json","view_paper":"https://pith.science/paper/3HA6UPTO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.06786&json=true","fetch_graph":"https://pith.science/api/pith-number/3HA6UPTOLQKVIHHAKAG2ITQS2R/graph.json","fetch_events":"https://pith.science/api/pith-number/3HA6UPTOLQKVIHHAKAG2ITQS2R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3HA6UPTOLQKVIHHAKAG2ITQS2R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3HA6UPTOLQKVIHHAKAG2ITQS2R/action/storage_attestation","attest_author":"https://pith.science/pith/3HA6UPTOLQKVIHHAKAG2ITQS2R/action/author_attestation","sign_citation":"https://pith.science/pith/3HA6UPTOLQKVIHHAKAG2ITQS2R/action/citation_signature","submit_replication":"https://pith.science/pith/3HA6UPTOLQKVIHHAKAG2ITQS2R/action/replication_record"}},"created_at":"2026-07-05T06:42:55.641452+00:00","updated_at":"2026-07-05T06:42:55.641452+00:00"}