{"id":"W4362558814","doi":"10.58837/chula.the.2020.141","title":"Using automatic speech recognition to assess Thai speech language fluency in montreal cognitive assessment (MoCA)","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Hidden Markov model; Fluency; Language model; Artificial intelligence; Natural language processing; Test set; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001234453,0.0006983628,0.0003234945,0.0009534436,0.0002090642,0.0006031984,0.000379044,0.0005272406,0.001285386],"category_scores_gemma":[0.003942975,0.0001288467,0.0004834217,0.0004123216,0.0002475152,0.0005113047,0.0004621908,0.000278229,0.0005012574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002799434,"about_ca_system_score_gemma":0.0005505962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009761353,"about_ca_topic_score_gemma":0.01028693,"domain_scores_codex":[0.9990739,0.0002986961,0.00008816659,0.0002239361,0.000250212,0.00006509086],"domain_scores_gemma":[0.9989459,0.0003708832,0.000104338,0.00007310032,0.0004587846,0.00004698236],"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":[0.001229739,0.0003189364,0.04449074,0.0005064157,0.000373334,0.0009387257,0.0007412495,0.02823169,0.214579,0.0008815745,0.0032143,0.7044944],"study_design_scores_gemma":[0.0002416134,0.002891157,0.2323294,0.0001027492,0.0005406413,0.004697314,0.0007529052,0.512955,0.2366251,0.001586313,0.006844413,0.0004334359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7800398,0.0006055003,0.2112645,0.0001463029,0.0001179163,0.00048987,0.001263532,0.002498536,0.00357404],"genre_scores_gemma":[0.9209037,0.0002148797,0.07599765,0.00008776017,0.00003645356,0.0003372234,0.001007584,0.00007881429,0.001335835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009761353,"threshold_uncertainty_score":0.01940912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.072513247186501,"score_gpt":0.3551032207596921,"score_spread":0.2825899735731912,"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."}}