{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:3EVKIYRQGHUDQAHZNAQDO2JCJW","short_pith_number":"pith:3EVKIYRQ","canonical_record":{"source":{"id":"1409.8558","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-09-30T14:20:29Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"e2fb20023c8a6905a357d65ee24785993b3e8c987ea50122922b722252654546","abstract_canon_sha256":"11700c6e591049ecf4841b1f7e4b76ff5a2baa65def8cf9b73c59ecb08d2423e"},"schema_version":"1.0"},"canonical_sha256":"d92aa4623031e83800f968203769224da81e9f8f49cc9f08a6ef8faa72af1ff0","source":{"kind":"arxiv","id":"1409.8558","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.8558","created_at":"2026-05-18T02:41:24Z"},{"alias_kind":"arxiv_version","alias_value":"1409.8558v1","created_at":"2026-05-18T02:41:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.8558","created_at":"2026-05-18T02:41:24Z"},{"alias_kind":"pith_short_12","alias_value":"3EVKIYRQGHUD","created_at":"2026-05-18T12:28:11Z"},{"alias_kind":"pith_short_16","alias_value":"3EVKIYRQGHUDQAHZ","created_at":"2026-05-18T12:28:11Z"},{"alias_kind":"pith_short_8","alias_value":"3EVKIYRQ","created_at":"2026-05-18T12:28:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:3EVKIYRQGHUDQAHZNAQDO2JCJW","target":"record","payload":{"canonical_record":{"source":{"id":"1409.8558","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-09-30T14:20:29Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"e2fb20023c8a6905a357d65ee24785993b3e8c987ea50122922b722252654546","abstract_canon_sha256":"11700c6e591049ecf4841b1f7e4b76ff5a2baa65def8cf9b73c59ecb08d2423e"},"schema_version":"1.0"},"canonical_sha256":"d92aa4623031e83800f968203769224da81e9f8f49cc9f08a6ef8faa72af1ff0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:41:24.211267Z","signature_b64":"4g0XZQVhlDD0AUV/WXcp2urNa7KQfpUavA/UzJgBlqsy0zFwa6JKFAAuVWDvhnQFoQBgl1JFgYFF+wFqlrtZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d92aa4623031e83800f968203769224da81e9f8f49cc9f08a6ef8faa72af1ff0","last_reissued_at":"2026-05-18T02:41:24.210797Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:41:24.210797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.8558","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:41:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"roS6jGr70NFNF5gUS1K6fIRdE/Ojj8FBZJzwYVYmC0p2hLNZ9UtEe1+G2HuyfqbnHtF2924xubCqusZC0spxCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T22:27:29.278071Z"},"content_sha256":"301b72947a72b17a5eb1a2b4b4595d7691a9f1a4871b319a409e3167e12d21b8","schema_version":"1.0","event_id":"sha256:301b72947a72b17a5eb1a2b4b4595d7691a9f1a4871b319a409e3167e12d21b8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:3EVKIYRQGHUDQAHZNAQDO2JCJW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.CL","authors_text":"Alan W. Black, Prasanna Kumar Muthukumar","submitted_at":"2014-09-30T14:20:29Z","abstract_excerpt":"Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral coefficients as the vocal tract parameterization of the speech signal. Mel Cepstral coefficients were never intended to work in a parametric speech synthesis framework, but as yet, there has been little success in creating a better parameterization that is more suited to synthesis. In this paper, we use deep learning algorithms to investigate a data-driven parameterization technique that is designed for the specific requirements of synthesis. We create an invertible, low-dimensional, noise-robust encoding of the Mel L"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.8558","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":""},"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":"2026-05-18T02:41:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UwXZ+jEHVXjc2aTprl8zpytSm/KuTZWuWbH4sJMszyAfYgRkI0dTo1+y8Dd/8Hu9MEcYehLA7yJDXqezy9yGDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T22:27:29.278776Z"},"content_sha256":"78767bc92d10d25223a8c80f133e98027df7965daa668d2f50cd44bfef21f927","schema_version":"1.0","event_id":"sha256:78767bc92d10d25223a8c80f133e98027df7965daa668d2f50cd44bfef21f927"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3EVKIYRQGHUDQAHZNAQDO2JCJW/bundle.json","state_url":"https://pith.science/pith/3EVKIYRQGHUDQAHZNAQDO2JCJW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3EVKIYRQGHUDQAHZNAQDO2JCJW/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":"2026-05-30T22:27:29Z","links":{"resolver":"https://pith.science/pith/3EVKIYRQGHUDQAHZNAQDO2JCJW","bundle":"https://pith.science/pith/3EVKIYRQGHUDQAHZNAQDO2JCJW/bundle.json","state":"https://pith.science/pith/3EVKIYRQGHUDQAHZNAQDO2JCJW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3EVKIYRQGHUDQAHZNAQDO2JCJW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:3EVKIYRQGHUDQAHZNAQDO2JCJW","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":"11700c6e591049ecf4841b1f7e4b76ff5a2baa65def8cf9b73c59ecb08d2423e","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-09-30T14:20:29Z","title_canon_sha256":"e2fb20023c8a6905a357d65ee24785993b3e8c987ea50122922b722252654546"},"schema_version":"1.0","source":{"id":"1409.8558","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.8558","created_at":"2026-05-18T02:41:24Z"},{"alias_kind":"arxiv_version","alias_value":"1409.8558v1","created_at":"2026-05-18T02:41:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.8558","created_at":"2026-05-18T02:41:24Z"},{"alias_kind":"pith_short_12","alias_value":"3EVKIYRQGHUD","created_at":"2026-05-18T12:28:11Z"},{"alias_kind":"pith_short_16","alias_value":"3EVKIYRQGHUDQAHZ","created_at":"2026-05-18T12:28:11Z"},{"alias_kind":"pith_short_8","alias_value":"3EVKIYRQ","created_at":"2026-05-18T12:28:11Z"}],"graph_snapshots":[{"event_id":"sha256:78767bc92d10d25223a8c80f133e98027df7965daa668d2f50cd44bfef21f927","target":"graph","created_at":"2026-05-18T02:41:24Z","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":"Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral coefficients as the vocal tract parameterization of the speech signal. Mel Cepstral coefficients were never intended to work in a parametric speech synthesis framework, but as yet, there has been little success in creating a better parameterization that is more suited to synthesis. In this paper, we use deep learning algorithms to investigate a data-driven parameterization technique that is designed for the specific requirements of synthesis. We create an invertible, low-dimensional, noise-robust encoding of the Mel L","authors_text":"Alan W. Black, Prasanna Kumar Muthukumar","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-09-30T14:20:29Z","title":"A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.8558","kind":"arxiv","version":1},"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":"sha256:301b72947a72b17a5eb1a2b4b4595d7691a9f1a4871b319a409e3167e12d21b8","target":"record","created_at":"2026-05-18T02:41:24Z","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":"11700c6e591049ecf4841b1f7e4b76ff5a2baa65def8cf9b73c59ecb08d2423e","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2014-09-30T14:20:29Z","title_canon_sha256":"e2fb20023c8a6905a357d65ee24785993b3e8c987ea50122922b722252654546"},"schema_version":"1.0","source":{"id":"1409.8558","kind":"arxiv","version":1}},"canonical_sha256":"d92aa4623031e83800f968203769224da81e9f8f49cc9f08a6ef8faa72af1ff0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d92aa4623031e83800f968203769224da81e9f8f49cc9f08a6ef8faa72af1ff0","first_computed_at":"2026-05-18T02:41:24.210797Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:41:24.210797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4g0XZQVhlDD0AUV/WXcp2urNa7KQfpUavA/UzJgBlqsy0zFwa6JKFAAuVWDvhnQFoQBgl1JFgYFF+wFqlrtZBg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:41:24.211267Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.8558","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:301b72947a72b17a5eb1a2b4b4595d7691a9f1a4871b319a409e3167e12d21b8","sha256:78767bc92d10d25223a8c80f133e98027df7965daa668d2f50cd44bfef21f927"],"state_sha256":"1133321927df92881b4acb8cf56ae644f6547b3f00475cb25f3fc2c29d10b257"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KbIiqQ9UVyfbSVkdISs3rMDzzKf1aHYj1wBhgjzHMxfshqFrnshlhDa6DH/4Kl5BbSRfN1Wm8i0V+6ULoK8aDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T22:27:29.282395Z","bundle_sha256":"9f138d24ec83f3eb069ac48074007ad956fb53d1d3055b664c1195c8ba15464a"}}