Machine Learning Predicts Accuracy in Eyewitnesses' Voices.
An important task in criminal justice is to evaluate the accuracy of eyewitness testimony. In this study, we examined if machine learning could be used to detect accuracy. Specifically, we examined if support vector machines (SVMs) could accurately classify testimony statements as correct or incorre...
| Publicado en: | Journal of Nonverbal Behavior Vol. 49; no. 1; pp. 155 - 170 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Mar2025
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=184390680&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 184390680 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 01915886 JNV jtl: Journal of Nonverbal Behavior issn: 01915886 maglogo: N pubinfo: dt: Mar2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 184390680 10.1007/s10919-024-00474-9 ppf: 155 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: Machine Learning Predicts Accuracy in Eyewitnesses' Voices. aug: au: Gustafsson, Philip U. Lachmann, Tim Laukka, Petri affil: https://ror.org/05f0yaq80 Department of Psychology, Stockholm University, 114 19, Stockholm, Sweden https://ror.org/048a87296 Department of Psychology, Uppsala University, Uppsala, Sweden su: Confidence Memory Criminal justice system Receiver operating characteristic curves Prompts (Psychology) Research funding Research evaluation Descriptive statistics Support vector machines Physiological aspects of speech Conceptual structures Machine learning Algorithms Regression analysis sug: subj: Confidence Memory Criminal justice system Other Justice, Public Order, and Safety Activities Receiver operating characteristic curves Prompts (Psychology) Research funding Research evaluation Descriptive statistics Support vector machines Physiological aspects of speech Conceptual structures Machine learning Algorithms Regression analysis keyword: Eyewitness accuracy Eyewitness testimony Forensic voice comparison Information and Computing Sciences Artificial Intelligence and Image Processing Psychology and Cognitive Sciences Psychology Non-verbal cues Eyewitness accuracy Eyewitness testimony Forensic voice comparison Information and Computing Sciences Artificial Intelligence and Image Processing Psychology and Cognitive Sciences Psychology Non-verbal cues ab: An important task in criminal justice is to evaluate the accuracy of eyewitness testimony. In this study, we examined if machine learning could be used to detect accuracy. Specifically, we examined if support vector machines (SVMs) could accurately classify testimony statements as correct or incorrect based purely on the nonverbal aspects of the voice. We analyzed 3,337 statements (76.61% accurate) from 51 eyewitness testimonies along 94 acoustic variables. We also examined the relative importance of each of the acoustic variables, using Lasso regression. Results showed that the machine learning algorithms were able to predict accuracy between 20 and 40% above chance level (AUC = 0.50). The most important predictors included acoustic variables related to the amplitude (loudness) of speech and the duration of pauses, with higher amplitude predicting correct recall and longer pauses predicting incorrect recall. Taken together, we find that machine learning methods are capable of predicting whether eyewitness testimonies are correct or incorrect with above-chance accuracy and comparable to human performance, but without detrimental human biases. This offers a proof-of-concept for machine learning in evaluations of eyewitness accuracy, and opens up new avenues of research that we hope might improve social justice. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|