{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001154143,0.0001169053,0.0001285444,0.0001105151,0.0001394187,0.0004635185,0.0002018228,0.00002792123,0.00002218907],"category_scores_gemma":[0.00004102537,0.00009068109,0.00002367453,0.0004021214,0.00002131273,0.0004384164,0.00004830551,0.0001551139,0.0001695829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004783854,"about_ca_system_score_gemma":0.00005698117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004561129,"about_ca_topic_score_gemma":0.000003075587,"domain_scores_codex":[0.9989671,0.00001871948,0.000116905,0.000333097,0.0003419955,0.0002221428],"domain_scores_gemma":[0.9995986,0.00007192451,0.00003718751,0.0001910166,0.00004550352,0.00005571771],"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":[0.00002123234,0.0008561609,0.0002090828,0.0006117775,0.0002022976,0.0002261002,0.001096839,0.01173388,0.9184603,0.0003248636,0.0003525497,0.06590489],"study_design_scores_gemma":[0.0001268757,0.0003525447,0.0002405338,0.001743746,0.00004583941,0.0000163966,0.000009035381,0.448866,0.5476871,0.0006941226,0.00001814119,0.0001996991],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3676213,0.0003489903,0.6263734,0.0008120612,0.0001778799,0.0001475831,6.275249e-7,0.0008643407,0.003653873],"genre_scores_gemma":[0.8039887,0.000001965904,0.1949024,0.0001326642,0.0001942673,0.00001398513,0.000001074907,0.00001366355,0.0007512406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4371321,"threshold_uncertainty_score":0.446972,"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."}}