{"as_of":"2026-08-09T16:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:69c676b7115efccc1e3cb11472ef854f5b0476dd9837edf69cb0901e765f204a","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T18:22:54.128084Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T18:22:50.261816Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.11845","snapshot_observed_at":"2026-08-02T18:22:50.261816Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:50.261816Z"},"links":{"cited_paper":"/paper/2603.11845","citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:c5456d4dcd354d38d4972a626a943015a186399fbb1f04f74e508c64258a1e77","observation_id":"0b9c5122-69cd-4293-acc4-f977cb9a3724","resolution":{"observed_at":"2026-08-02T18:22:50.261816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2603.11845/citation-record","integrity":"/paper/2603.11845/integrity","json":"/paper/2603.11845/citation-record.json","paper":"/paper/2603.11845"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:50.182190Z","title":"Since the 1970s, several families of solutions have been pro- posed to address this complex inverse problem","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:50.182190Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:116828929b5b128c5d8677352c28499d1cd862192f7ea4909fca1145bbd7f772","observation_id":"fc12be3f-a65e-43b7-8e8b-0dc8de3caf88","resolution":{"observed_at":"2026-08-02T18:22:50.182190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:50.221078Z","title":"How- ever, speech recorded in an MRI machine, even if it is denoised before use, is still far from speech produced in a quiet envi- ronment","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:50.221078Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:7ec5cdd0ea6787358ca88c925d2a1967c943d8119af6441441b09c1985855235","observation_id":"eba6292b-2a54-49d3-8cbf-768cb752784e","resolution":{"observed_at":"2026-08-02T18:22:50.221078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:50.322507Z","title":"The first corpus was recorded at the Centre Hospitalier R ´egional Universitaire (CHRU) de Nancy and contains approximately 2.5 hours of data","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:50.322507Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:d4fc1cabec6e42670b9cd535cdab3c37b4c2d2be3717711d92d842a87fbddf78","observation_id":"9961f304-fd06-4ccb-89f0-73ac0e9b5403","resolution":{"observed_at":"2026-08-02T18:22:50.322507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:50.613321Z","title":"Apr`es une heure","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:50.613321Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:b12569f20c1166e4263a86f0c174c56299ff410de986618603c31cabeb61ef46","observation_id":"de8faf10-6d68-46c1-9fe5-878ba5eb26dd","resolution":{"observed_at":"2026-08-02T18:22:50.613321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:51.187324Z","title":"The M2M configuration remains the best, with an average RMSE of 1.51 mm and a median of 1.33 mm","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:51.187324Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:959d0deeecf373ef9c2cc288dadda56f0e21c4f8d0e62a8820864c870e4a9cf6","observation_id":"1d84a803-1472-4048-97f4-154f21b87e0f","resolution":{"observed_at":"2026-08-02T18:22:51.187324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:51.290549Z","title":"This configuration corresponds to that of our previous work on in- version, using denoised speech for both training and testing","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:51.290549Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:11eb78aee5a746edcf8158ae98718ee3096b93f81d3feb411e4c3ca7c16d4aba","observation_id":"09219410-0288-40c1-b339-4744823daed0","resolution":{"observed_at":"2026-08-02T18:22:51.290549Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:50.728792Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:50.728792Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:723dc17a81f364bc5f6967d36c7094a1babc0a01df40b17fcc169c15d271238b","observation_id":"4e81944a-307e-4151-9c51-aef66e35ae66","resolution":{"observed_at":"2026-08-02T18:22:50.728792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:50.869127Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:50.869127Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:562b195990cb0245e8b81acddbea4b25c1adbd92f05017482491f912b3258463","observation_id":"8195e381-2b80-4e3c-b4ce-476aabff5d03","resolution":{"observed_at":"2026-08-02T18:22:50.869127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:51.035075Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:51.035075Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:54077db813d7b6da4c81e66a3447f9c230567743340510e2d18d773f17377e82","observation_id":"14ceba48-00df-47a4-b12f-0ffec3f47186","resolution":{"observed_at":"2026-08-02T18:22:51.035075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:52.833204Z","title":"Speaker dependent acoustic-to-articulatory inversion using real-time MRI of the vocal tract,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:52.833204Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:06609bb3766c607bf9463a643748690bf4ca64559a50371007a8d398fb2e1eab","observation_id":"2fd82ddc-4aac-430b-adaa-dff96a4b9319","resolution":{"observed_at":"2026-08-02T18:22:52.833204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:52.923065Z","title":"Preprocessing for acoustic-to-articulatory inversion using real-time mri movies of japanese speech,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:52.923065Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:c08f40b762d47b728cdce9d684a8d14e918bb56477e02c85462d36c1a51490f3","observation_id":"cb1d9df5-cc72-4f05-a893-c13cd3dfddbc","resolution":{"observed_at":"2026-08-02T18:22:52.923065Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:51.422579Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:51.422579Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:7e78072c7883aef610ee7d31e6c6030ac72a9c268d1cc4da97885580660fbeae","observation_id":"a9a14a67-2d29-42bf-bbdb-68b0a94301a3","resolution":{"observed_at":"2026-08-02T18:22:51.422579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:51.593298Z","title":"A multi-channel/multi-speaker articulatory database for continuous speech recognition research,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:51.593298Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:fd490c84cface7d8347c8cfce5a4f430509892aca70ecb18f13f913f18d74642","observation_id":"2fa62f4b-e1b5-4ad3-89f3-768295d37a4c","resolution":{"observed_at":"2026-08-02T18:22:51.593298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:51.752564Z","title":"X-ray mi- crobeam speech production database,","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:51.752564Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:2dc53d2492705807c9b5c3a6f1666a013bdecaadccb47cfc02ebdf0752f40236","observation_id":"9dcebb0b-eb07-4d56-95e6-dc674c5bbab4","resolution":{"observed_at":"2026-08-02T18:22:51.752564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:51.873810Z","title":"Acoustic-to-articulatory inversion mapping with gaussian mixture model","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:51.873810Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:c9646ee4fb8f7c19dd5882e4c602d42fd1d980b2fa8ff0cb9cb491b8450442fc","observation_id":"34d060a0-66db-4338-a63c-9fe3c21ac810","resolution":{"observed_at":"2026-08-02T18:22:51.873810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:51.999557Z","title":"Estimation of articulatory movements from speech acoustics using an hmm-based speech production model,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:51.999557Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:b11b788947a3f877c379f3cf8e8be327fdf72d48db9f66a7fd0cf8f593d6b4a7","observation_id":"bcafdc29-6de6-4f9c-86db-6e4c87065cb5","resolution":{"observed_at":"2026-08-02T18:22:51.999557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:52.125276Z","title":"A deep recurrent approach for acoustic-to-articulatory inversion,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:52.125276Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:3c7c194ef28fc31fce5032799dd73480f36c7f376c5afe86328c8a3df4fc0c14","observation_id":"8c34b210-4097-4b44-8bd2-03db93de8791","resolution":{"observed_at":"2026-08-02T18:22:52.125276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:52.252484Z","title":"Independent and Automatic Evaluation of Speaker-Independent Acoustic-to- Articulatory Reconstruction,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:52.252484Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:8f923558a7f081186ed4abbf3619e18597e6e644ca57eea82c41c686c7f37195","observation_id":"46e8d844-8c2c-4f9a-99a4-c7ec40902264","resolution":{"observed_at":"2026-08-02T18:22:52.252484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:52.368057Z","title":"Speaker-independent acoustic-to-articulatory speech inversion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:52.368057Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:09733061c8b8216b14b74093e1f4ffbe75c22c54c54938d332c4ad2238914db9","observation_id":"52b8089f-ed1d-4ca4-9845-a69b2fea6839","resolution":{"observed_at":"2026-08-02T18:22:52.368057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:52.547849Z","title":"The secret source: In- corporating source features to improve acoustic-to-articulatory speech inversion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:52.547849Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:298a53af8ff2da7b5b50b45ecf64a499badb241920139e8cd8f2b58ed157da92","observation_id":"c4c6e208-3620-4063-b28e-c1ea6e3cbd34","resolution":{"observed_at":"2026-08-02T18:22:52.547849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:52.696552Z","title":"Analysis of speech production real-time mri,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:52.696552Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:154ee02f710c0fe87184ed890a2cc332a09b2b7eef336841985d454f17a0d6c2","observation_id":"2636f26b-94ac-4eaf-a5a1-2bd89f24cff4","resolution":{"observed_at":"2026-08-02T18:22:52.696552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.803281Z","title":"Multimodal dataset of real-time 2D and static 3D MRI of healthy French speakers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.803281Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:f77908d22fed9c5f96fc58579835a8161a2ec6f4e865be13b5ee2184b21ad25a","observation_id":"9edc5bab-6874-4e21-90de-eda76b05e526","resolution":{"observed_at":"2026-08-02T18:22:53.803281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.11845","snapshot_observed_at":"2026-08-02T18:22:50.261816Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:50.261816Z"},"links":{"cited_paper":"/paper/2603.11845","citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:c5456d4dcd354d38d4972a626a943015a186399fbb1f04f74e508c64258a1e77","observation_id":"0b9c5122-69cd-4293-acc4-f977cb9a3724","resolution":{"observed_at":"2026-08-02T18:22:50.261816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.037002Z","title":"Speech2rtmri: Speech-guided diffusion model for real-time mri video of the vocal tract during speech,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.037002Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:e1d4ac19f09769c7b2bd31c326668b0a2b84cda72747d3bd5bd7a4a3215678f5","observation_id":"1f201044-3f5b-4f86-a50e-ba12db3b53b6","resolution":{"observed_at":"2026-08-02T18:22:53.037002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.097956Z","title":"Automatic segmentation of vocal tract articulators in real-time magnetic resonance imaging,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.097956Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:b32c3b2264acaabceefbf246369b473e22e281a7f4e2b17d07f7e8dbdda538e5","observation_id":"0d7a1521-4bc4-47c0-b485-03e43fd208e1","resolution":{"observed_at":"2026-08-02T18:22:53.097956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:50.400122Z","title":"Both phonetic segmentations were carefully reviewed to ensure they were strictly identical across the two corpora","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:50.400122Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:356f7378bc85a7b411f4e8b76167a9baef448087e2055b0203d7617d7eb3f9cd","observation_id":"c8193d2c-e022-4131-87c7-ef4233b906de","resolution":{"observed_at":"2026-08-02T18:22:50.400122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.188555Z","title":"Complete Reconstruc- tion of the Tongue Contour Through Acoustic to Articulatory Inversion Using Real-Time MRI Data,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.188555Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:9e9e86cb6dc1e5b42dcfdda9b9a6bcfad3913d01aa21dc40cafb60661e48d549","observation_id":"080d4b77-b26a-4149-affe-5d5c1ec6fcd2","resolution":{"observed_at":"2026-08-02T18:22:53.188555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.266565Z","title":"Reconstruction of the Complete V ocal Tract Contour Through Acoustic to Articulatory Inversion Using Real-Time MRI Data,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.266565Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:8023c02c0a6caa51c2422f85a26d5551e5533145dfe38167454902942faaff8f","observation_id":"19b23fed-29eb-4920-9eb4-a35f05f35b08","resolution":{"observed_at":"2026-08-02T18:22:53.266565Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.351970Z","title":"Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.351970Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:091bdb9332328ca7a1e5367d2b92583912e85d67d4985a736981fd625920df9e","observation_id":"f5235915-146e-41e5-bee1-6cf8b4ab7c4a","resolution":{"observed_at":"2026-08-02T18:22:53.351970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.404063Z","title":"Self-supervised models of speech infer universal articulatory kinematics,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.404063Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:b493cdfa18e836db1896d546ad62a6d2693b54485b4ebac0089021f1debc742f","observation_id":"8c288bc4-f418-4db9-98ae-66bccbd6e5dc","resolution":{"observed_at":"2026-08-02T18:22:53.404063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.480097Z","title":"wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.480097Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:feae0809f41535d1f5d1b0f6e9b87e04d6b231792ee2701692319e2cfa4911b4","observation_id":"21619794-ab7c-4775-8615-7668ffa11882","resolution":{"observed_at":"2026-08-02T18:22:53.480097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.559803Z","title":"Wavlm: Large-scale self- supervised pre-training for full stack speech processing,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.559803Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:d7b4028a7779c807e9e9eb059a57d52e84244ce5c70184117dc96ea522dd17d1","observation_id":"164ead96-2a0b-4171-a544-a3736e247353","resolution":{"observed_at":"2026-08-02T18:22:53.559803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.637789Z","title":"Improv- ing speech inversion through self-supervised embeddings and en- hanced tract variables,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.637789Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:f5f24345c61bc7c806487b317889e9fa3c74494e2ba914ab482b09721f802872","observation_id":"4b067e34-c436-4951-9aa8-74e8c82698f5","resolution":{"observed_at":"2026-08-02T18:22:53.637789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.02735","last_updated":"2022-10-03T20:50:54Z","snapshot_observed_at":"2026-07-06T12:05:22.076764Z","submitted_at":"2021-11-04T10:39:06Z","title":"A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.02735","snapshot_observed_at":"2026-08-02T18:22:53.721498Z","title":"A fine-tuned wav2vec 2.0/hubert benchmark for speech emotion recognition, speaker verification and spoken language understanding,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.721498Z"},"links":{"cited_paper":"/paper/2111.02735","citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:e0d93f5faee49e50d1a5ccea9548132c8612aaff228c2f2f40081ca4753f3241","observation_id":"c17e9a26-d61c-41d8-b06f-acc2587c7b15","resolution":{"observed_at":"2026-08-02T18:22:53.721498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.860803Z","title":"The influence of acoustics on speech production: A noise-induced stress phenomenon known as the lombard reflex,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.860803Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:4c230f5b052481e4fb4b33aebfb374414da3b33ef8e72e2432b1dcfd5283d230","observation_id":"8b8bb7af-fec1-4307-b590-23c37f2ab248","resolution":{"observed_at":"2026-08-02T18:22:53.860803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.915118Z","title":"A general flexible frame- work for the handling of prior information in audio source sepa- ration,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.915118Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:9988acd78caa3cb018c3495da7523c849595471e5924a7b5c98b93c4c6725616","observation_id":"0b5d8c48-c1ba-4d13-b03c-97b76abd2d41","resolution":{"observed_at":"2026-08-02T18:22:53.915118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:53.961906Z","title":"De l’importance de l’homog ´en´eisation des conventions de transcription pour l’alignement automatique de corpus oraux de parole spontan ´ee,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:53.961906Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:a1b3707b371120b9616dd3cc90da4dd87807e74f82b9a7d3fc7dbb9a7288ffff","observation_id":"bc07674c-7188-47ea-8599-ea419a1f2262","resolution":{"observed_at":"2026-08-02T18:22:53.961906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:54.009244Z","title":"Montreal forced aligner: Trainable text-speech align- ment using kaldi","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:54.009244Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:ac4238637c077ace1023226c8295b30f27d33aaf539cd6b84a145718bac4f01d","observation_id":"5c484670-bf95-42c7-9cc7-16c2651170d8","resolution":{"observed_at":"2026-08-02T18:22:54.009244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:54.069292Z","title":"Pattern matching: The gestalt approach,","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:54.069292Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:ebab7528c1cb4ad2246e5088143fe3a18c0898c5c6a1d6b001cb872cdc11e26e","observation_id":"979567ff-b331-4745-898e-615fa66cdd6c","resolution":{"observed_at":"2026-08-02T18:22:54.069292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T18:22:54.128084Z","title":"M ¨uller,Fundamentals of music processing: Audio, analysis, algorithms, applications","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-02T18:22:54.128084Z"},"links":{"citing_paper":"/paper/2603.11845"},"observation_digest":"sha256:9a1dc7f9e150ed32e6cea64e160ef5aed7e9db274ba5d34e273d71ba99583417","observation_id":"dbdbbc1e-149d-4ad3-a2fc-5debf42606a5","resolution":{"observed_at":"2026-08-02T18:22:54.128084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.11845","last_updated":"2026-07-22T16:59:57Z","latest_version":2,"primary_category":"eess.AS","snapshot_observed_at":"2026-08-07T02:41:18.008375Z","submitted_at":"2026-03-12T12:09:22Z","title":"Acoustic-to-Articulatory Inversion of Clean Speech Using an MRI-Trained Model"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":40,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":40},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2603.11845."}