{"id":"W1597208282","doi":"10.1109/icassp.2015.7178806","title":"Speaker change point detection using deep neural nets","year":2015,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Speaker diarisation; Change detection; Computer science; Speech recognition; Frame (networking); Artificial neural network; Point (geometry); Set (abstract data type); Speaker recognition; Test set; Speech processing; Word error rate; Voice activity detection; Artificial intelligence; Mathematics; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001330543,0.0008346555,0.0004767687,0.0009710555,0.0003059816,0.00063801,0.0006587047,0.0005621451,0.001757656],"category_scores_gemma":[0.003618191,0.0002724643,0.0003890297,0.0004115753,0.0002536826,0.0008288276,0.0005398001,0.0008702551,0.0006401953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008357542,"about_ca_system_score_gemma":0.0004384195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01131947,"about_ca_topic_score_gemma":0.01421382,"domain_scores_codex":[0.9992482,0.000173023,0.00004160285,0.0002057528,0.0002249674,0.0001063842],"domain_scores_gemma":[0.9980887,0.00108991,0.0001350432,0.0001303811,0.0004775817,0.00007840738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001600154,0.0003684351,0.02336141,0.0001770464,0.0002772427,0.000413433,0.0003076057,0.2489023,0.1175442,0.001644554,0.003100242,0.6023034],"study_design_scores_gemma":[0.00001652991,0.0001167036,0.005466321,0.000007617892,0.00003374987,0.00008955964,0.00004467218,0.9577801,0.03504663,0.0006063996,0.0007724821,0.00001926041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5766231,0.001035541,0.4124562,0.0004028995,0.0001458302,0.0001126895,0.0006418597,0.003327333,0.005254503],"genre_scores_gemma":[0.9204376,0.0001306753,0.07643013,0.00009926254,0.00002669674,0.00002657436,0.0006066903,0.00007620629,0.002166222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01131947,"threshold_uncertainty_score":0.02250713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1755712714818029,"score_gpt":0.2864213862817155,"score_spread":0.1108501147999126,"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."}}