{"id":"W2678453873","doi":"10.1109/ccece.2017.7946643","title":"Feature fusion techniques based training MLP for speaker identification system","year":2017,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Feature (linguistics); Speech recognition; Artificial intelligence; Speaker identification; Identification (biology); Speaker recognition; Pattern recognition (psychology); Feature extraction; Speaker diarisation; Training (meteorology); Training set","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":[0.0004766479,0.00008025901,0.0001038087,0.00007432574,0.0004532137,0.000596982,0.0006408762,0.0000744052,0.00001820733],"category_scores_gemma":[0.0001177848,0.00006575775,0.00007700293,0.00004186985,0.00001959605,0.0004077823,0.00004766666,0.00004286659,0.00003843358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003033475,"about_ca_system_score_gemma":0.00003079555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007071874,"about_ca_topic_score_gemma":0.00001055738,"domain_scores_codex":[0.9993189,0.00002397447,0.0001128988,0.0002622355,0.0001481435,0.0001338397],"domain_scores_gemma":[0.998962,0.00006665444,0.0001416621,0.0006631736,0.0001184642,0.00004808548],"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":[0.000005620675,0.00002076523,0.00004202632,0.00004512836,0.000005877948,0.000003409068,0.000110708,1.643356e-7,0.01733539,0.02122623,0.006750699,0.954454],"study_design_scores_gemma":[0.0004837839,0.00006381387,0.005641522,0.000248308,0.00001934011,0.0000258032,0.0003309766,0.2061659,0.7258407,0.001211549,0.05956806,0.000400206],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001049367,0.000005644518,0.9709856,0.004188524,0.0003069871,0.0003224621,0.000004493613,0.0005025762,0.02263438],"genre_scores_gemma":[0.5894441,0.000001156241,0.4077189,0.0002184815,0.00009533959,0.00007839405,0.000005015306,0.000007257368,0.002431356],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9540538,"threshold_uncertainty_score":0.5756711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0600633129292889,"score_gpt":0.2943440950155201,"score_spread":0.2342807820862312,"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."}}