{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:HG27YDUONVGRGYZOLS47UTOEOS","short_pith_number":"pith:HG27YDUO","canonical_record":{"source":{"id":"1711.01694","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2017-11-06T01:55:45Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"e75325642acc33f3591b27d68ee47ac193045a8d320ee7a1102da97710613eac","abstract_canon_sha256":"b103f0906c7c71796bdf67c6b2e7e077ad62add3be7da33c25007c8b37972982"},"schema_version":"1.0"},"canonical_sha256":"39b5fc0e8e6d4d13632e5cb9fa4dc474b43f9baaf26588f9c05437bf85f41e1c","source":{"kind":"arxiv","id":"1711.01694","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.01694","created_at":"2026-05-18T00:23:16Z"},{"alias_kind":"arxiv_version","alias_value":"1711.01694v2","created_at":"2026-05-18T00:23:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01694","created_at":"2026-05-18T00:23:16Z"},{"alias_kind":"pith_short_12","alias_value":"HG27YDUONVGR","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"HG27YDUONVGRGYZO","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"HG27YDUO","created_at":"2026-05-18T12:31:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:HG27YDUONVGRGYZOLS47UTOEOS","target":"record","payload":{"canonical_record":{"source":{"id":"1711.01694","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2017-11-06T01:55:45Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"e75325642acc33f3591b27d68ee47ac193045a8d320ee7a1102da97710613eac","abstract_canon_sha256":"b103f0906c7c71796bdf67c6b2e7e077ad62add3be7da33c25007c8b37972982"},"schema_version":"1.0"},"canonical_sha256":"39b5fc0e8e6d4d13632e5cb9fa4dc474b43f9baaf26588f9c05437bf85f41e1c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:16.451075Z","signature_b64":"LHSkCk61nNPkZBHaIViOXezPoR7oqp6DhNPEnObP8PAiTeStdwd4usg9XBORhVJP3ZDQ6ZUdu2Kdcxj0PQ2OCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39b5fc0e8e6d4d13632e5cb9fa4dc474b43f9baaf26588f9c05437bf85f41e1c","last_reissued_at":"2026-05-18T00:23:16.450290Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:16.450290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.01694","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:23:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UvU8Xr8EJWWPDKlroN+q5ROLHIfsJcqRwCAUzWeVIhrobe9XsGmHdpszoJo3eXzRnL6inQH75qA4SpyLxQs8DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:36:11.661258Z"},"content_sha256":"3a639241e19b76b869f9d726fe8bbc69230767c89b09933f35a4f220ff371c6a","schema_version":"1.0","event_id":"sha256:3a639241e19b76b869f9d726fe8bbc69230767c89b09933f35a4f220ff371c6a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:HG27YDUONVGRGYZOLS47UTOEOS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multilingual Speech Recognition With A Single End-To-End Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"eess.AS","authors_text":"Bo Li, Eugene Weinstein, Kanishka Rao, Pedro Moreno, Ron J. Weiss, Shubham Toshniwal, Tara N. Sainath","submitted_at":"2017-11-06T01:55:45Z","abstract_excerpt":"Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01694","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:23:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1RIseecl5Uc5ipmo1+MbuFunXEkbWCbGjxo3oY0Iu0J7lFjfpJxdsDkcHE5RBrmdtZ74ONS+INSK65jnR3w/AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:36:11.662013Z"},"content_sha256":"87ac36099c4177ebb53260606fd0dd31550777f06a83ff46d084e32cea416f1d","schema_version":"1.0","event_id":"sha256:87ac36099c4177ebb53260606fd0dd31550777f06a83ff46d084e32cea416f1d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HG27YDUONVGRGYZOLS47UTOEOS/bundle.json","state_url":"https://pith.science/pith/HG27YDUONVGRGYZOLS47UTOEOS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HG27YDUONVGRGYZOLS47UTOEOS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T02:36:11Z","links":{"resolver":"https://pith.science/pith/HG27YDUONVGRGYZOLS47UTOEOS","bundle":"https://pith.science/pith/HG27YDUONVGRGYZOLS47UTOEOS/bundle.json","state":"https://pith.science/pith/HG27YDUONVGRGYZOLS47UTOEOS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HG27YDUONVGRGYZOLS47UTOEOS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:HG27YDUONVGRGYZOLS47UTOEOS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b103f0906c7c71796bdf67c6b2e7e077ad62add3be7da33c25007c8b37972982","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2017-11-06T01:55:45Z","title_canon_sha256":"e75325642acc33f3591b27d68ee47ac193045a8d320ee7a1102da97710613eac"},"schema_version":"1.0","source":{"id":"1711.01694","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.01694","created_at":"2026-05-18T00:23:16Z"},{"alias_kind":"arxiv_version","alias_value":"1711.01694v2","created_at":"2026-05-18T00:23:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.01694","created_at":"2026-05-18T00:23:16Z"},{"alias_kind":"pith_short_12","alias_value":"HG27YDUONVGR","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"HG27YDUONVGRGYZO","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"HG27YDUO","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:87ac36099c4177ebb53260606fd0dd31550777f06a83ff46d084e32cea416f1d","target":"graph","created_at":"2026-05-18T00:23:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific. In contrast, sequence-to-sequence models are well suited for multilingual ASR because they encapsulate an acoustic, pronunciation and language model jointly in a single network. In this work we present a single sequence-to-sequence ASR model trained on 9 different Indian languages, which have very little overlap in their scripts. Specifically, we take a union of language-specific grapheme sets and","authors_text":"Bo Li, Eugene Weinstein, Kanishka Rao, Pedro Moreno, Ron J. Weiss, Shubham Toshniwal, Tara N. Sainath","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2017-11-06T01:55:45Z","title":"Multilingual Speech Recognition With A Single End-To-End Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.01694","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3a639241e19b76b869f9d726fe8bbc69230767c89b09933f35a4f220ff371c6a","target":"record","created_at":"2026-05-18T00:23:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b103f0906c7c71796bdf67c6b2e7e077ad62add3be7da33c25007c8b37972982","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2017-11-06T01:55:45Z","title_canon_sha256":"e75325642acc33f3591b27d68ee47ac193045a8d320ee7a1102da97710613eac"},"schema_version":"1.0","source":{"id":"1711.01694","kind":"arxiv","version":2}},"canonical_sha256":"39b5fc0e8e6d4d13632e5cb9fa4dc474b43f9baaf26588f9c05437bf85f41e1c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"39b5fc0e8e6d4d13632e5cb9fa4dc474b43f9baaf26588f9c05437bf85f41e1c","first_computed_at":"2026-05-18T00:23:16.450290Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:16.450290Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LHSkCk61nNPkZBHaIViOXezPoR7oqp6DhNPEnObP8PAiTeStdwd4usg9XBORhVJP3ZDQ6ZUdu2Kdcxj0PQ2OCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:16.451075Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.01694","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3a639241e19b76b869f9d726fe8bbc69230767c89b09933f35a4f220ff371c6a","sha256:87ac36099c4177ebb53260606fd0dd31550777f06a83ff46d084e32cea416f1d"],"state_sha256":"c805e25d54e06d312cb28cb8a03db2bfe8e360a578632b679c1540d4546eb93d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pb2Oi75qkVG0odaz5PZRRt+EgIy5UsiboTTFe/FLKS1mL01j97IPjHgwiNgVwQKM4xwsfpiJCi6eQLejWovjBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T02:36:11.665760Z","bundle_sha256":"222a0df077fed6b8549d92c4a18dbd8c64661b15ae03220fbcca336520d1a780"}}