{"id":"W2806367735","doi":"10.1609/aaai.v32i1.12140","title":"Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Conversation; Computer science; Task (project management); Component (thermodynamics); Speaker diarisation; Speech recognition; Speaker recognition; Artificial intelligence; Natural language processing; Linguistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006392052,0.0001739535,0.0001851293,0.0001006411,0.0001925282,0.0002845177,0.001291285,0.00006559857,0.00005653812],"category_scores_gemma":[0.000248101,0.0001101697,0.00004979446,0.0004257846,0.0003800369,0.0006871814,0.0003850278,0.0002270231,0.00004635893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002574697,"about_ca_system_score_gemma":0.00006156102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001686022,"about_ca_topic_score_gemma":0.00009860878,"domain_scores_codex":[0.9985029,0.00002996813,0.0004197684,0.0004340703,0.0003473811,0.0002659193],"domain_scores_gemma":[0.9990048,0.00006857837,0.0001694631,0.0002883489,0.000401118,0.00006764149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001321558,0.0002528644,0.0001759591,0.0000397613,0.00002664516,0.000001796035,0.01061661,0.0004954927,0.0106283,0.5653155,0.0004706301,0.4118443],"study_design_scores_gemma":[0.00003559733,0.00004908147,0.00005626153,0.00005911738,0.00000597845,0.000006044065,0.0007708693,0.8789631,0.05744814,0.06243799,0.00004159706,0.0001262938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4782179,0.00008128081,0.4802324,0.0279615,0.001239359,0.0014444,0.00009621427,0.0001765784,0.0105503],"genre_scores_gemma":[0.9933143,0.00003496428,0.005906825,0.0006322519,0.00005263921,0.00001783988,0.00000150516,0.000007136995,0.00003248993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8784676,"threshold_uncertainty_score":0.4492589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2417889338420326,"score_gpt":0.3237499481515659,"score_spread":0.08196101430953323,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}