{"id":"W2015068436","doi":"10.1109/icra.2012.6224729","title":"WISS, a speaker identification system for mobile robots","year":2012,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Speaker identification; Identification (biology); Speech recognition; Speaker recognition; Mobile robot; Noise (video); Robot; Tracking (education); Signal-to-noise ratio (imaging); Speaker diarisation; Artificial intelligence; Pattern recognition (psychology); Telecommunications; Image (mathematics)","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":[0.0002654391,0.00005633393,0.0000647913,0.00003372675,0.00009040567,0.0001548577,0.0002705481,0.0000253565,0.000006758538],"category_scores_gemma":[0.00001130491,0.0000462048,0.00003236017,0.0001372093,0.000007559742,0.0008171541,0.00004429844,0.00002140854,0.0001661314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003097028,"about_ca_system_score_gemma":0.00001714406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003068225,"about_ca_topic_score_gemma":8.491326e-7,"domain_scores_codex":[0.9993932,0.00000928352,0.0001287543,0.0001471726,0.0001038253,0.0002178258],"domain_scores_gemma":[0.9995513,0.00002313078,0.00005709736,0.0002449113,0.0000549717,0.00006859851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001229033,0.0003299283,0.00614576,0.0006332159,0.00004868009,0.000002473914,0.002669284,0.0003219614,0.2180935,0.09747548,0.01909245,0.655175],"study_design_scores_gemma":[0.0002607068,0.00002727388,0.002529216,0.00003691365,0.00000813444,0.00003008353,0.0002171791,0.008787382,0.9742202,0.0005783602,0.01309737,0.0002071639],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01208524,0.00033655,0.9834911,0.000118629,0.0005645728,0.000202764,4.849221e-7,0.0002374042,0.002963301],"genre_scores_gemma":[0.8866106,8.857396e-7,0.1118758,0.00007085088,0.0001622348,0.00006890123,0.000001556799,0.000004555497,0.0012047],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8745253,"threshold_uncertainty_score":0.2135339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767867441341413,"score_gpt":0.2645842547448495,"score_spread":0.2469055803314354,"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."}}