{"id":"W3215501073","doi":"","title":"Is Speech Emotion Recognition Language-Independent? Analysis of English and Bangla Languages using Language-Independent Vocal Features","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bengali; Disgust; Sadness; Computer science; Speech recognition; First language; Anger; Happiness; Set (abstract data type); Natural language processing; Artificial intelligence; Linguistics; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003918381,0.0004047625,0.0006960594,0.001128938,0.0001078564,0.00009127579,0.0002757034,0.0008404051,0.002741714],"category_scores_gemma":[0.0001360771,0.0004839918,0.000534234,0.0009922335,0.0001170989,0.0001914391,0.0003909711,0.000819616,0.00002709148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001703622,"about_ca_system_score_gemma":0.00007640483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002838473,"about_ca_topic_score_gemma":0.001576163,"domain_scores_codex":[0.9973601,0.0005078629,0.0003693662,0.001189037,0.0002135373,0.0003600947],"domain_scores_gemma":[0.9979249,0.0001081651,0.0004982325,0.0007090324,0.0005830734,0.0001766568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.002886681,0.007731061,0.1629465,0.00403076,0.06193243,0.02727848,0.4554784,0.03136104,0.02280063,0.004576046,0.003002413,0.2159755],"study_design_scores_gemma":[0.01091674,0.0005586904,0.3517358,0.002118366,0.0403069,0.000339101,0.5175668,0.03488097,0.03454453,0.001854382,0.0001418595,0.005035871],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849079,0.0007742719,0.004973457,0.00001793884,0.001028639,0.0003437895,0.0004155777,0.0001431123,0.007395372],"genre_scores_gemma":[0.9956456,0.0002200673,0.0003308576,0.0001266787,0.000309063,0.000001522648,0.001472545,0.00004382019,0.001849803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2109397,"threshold_uncertainty_score":0.9997612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.062669691976912,"score_gpt":0.2632425577701508,"score_spread":0.2005728657932388,"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."}}