{"id":"W4390496134","doi":"10.1371/journal.pone.0296452","title":"Estimating speaker direction on a humanoid robot with binaural acoustic signals","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Humanoid robot; Binaural recording; Computer science; Robot; Acoustic source localization; Speech recognition; Bayesian probability; Sound localization; Latency (audio); Artificial intelligence; Acoustics; Sound (geography); Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004575771,0.0006043953,0.0006033473,0.0004282999,0.00028013,0.0005155961,0.0005048056,0.0005313002,0.00275498],"category_scores_gemma":[0.001357518,0.0003009209,0.0002711789,0.0003260022,0.0003038441,0.0004956356,0.0009953125,0.0003699148,0.0007775489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001902183,"about_ca_system_score_gemma":0.0006868401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003942241,"about_ca_topic_score_gemma":0.007834047,"domain_scores_codex":[0.9997022,0.00009049367,0.00001185509,0.00007865825,0.00008403863,0.00003288133],"domain_scores_gemma":[0.9995905,0.0001763,0.00005048002,0.00005396398,0.00009611288,0.00003258884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001525235,0.0001408742,0.003668746,0.0003911467,0.0001237143,0.0006564108,0.0009094477,0.1168507,0.4907379,0.0008358489,0.0008705357,0.3832894],"study_design_scores_gemma":[0.0001022045,0.0007131824,0.01367704,0.00004729968,0.0001059144,0.0009930385,0.0006537446,0.8626481,0.1154043,0.002010094,0.003537283,0.0001078856],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3295534,0.0002456663,0.6664433,0.0001270527,0.00003779503,0.00008259085,0.0001245915,0.00174934,0.001636301],"genre_scores_gemma":[0.7684928,0.0001705929,0.2281431,0.00005044023,0.00001680556,0.00008229373,0.0001617698,0.0001101399,0.002772149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003942241,"threshold_uncertainty_score":0.009216368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03875740164119972,"score_gpt":0.2453545390355157,"score_spread":0.206597137394316,"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."}}