Are super-face-recognisers also super-voice-recognisers? Evidence from cross-modal identification tasks

Individual differences in face identification ability range from prosopagnosia to super-recognition. The current study examined whether face identification ability predicts voice identification ability (participants: N = 529). Superior-face-identifiers (exceptional at face memory and matching), superior-face-recognisers (exceptional at face memory only), superior-face-matchers (exceptional face matchers only), and controls completed the Bangor Voice Matching Test, Glasgow Voice Memory Test, and a Famous Voice Recognition Test. Meeting predictions, those possessing exceptional face memory and matching skills outperformed typical-range face groups at voice memory and voice matching respectively. Proportionally more super-face-identifiers also achieved our super-voice-recogniser criteria on two or more tests. Underlying cross-modality (voices vs. faces) and cross-task (memory vs. perception) mechanisms may therefore drive superior performances. Dissociations between Glasgow Voice Memory Test voice and bell recognition also suggest voice-specific effects to match those found with faces. These findings have applied implications for policing, particularly in cases when only suspect voice clips are available.

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The positive relationship between face identification and facial emotion perception ability extends to super-recognisers

10.31234/osf.io/4ktrj ◽

2020 ◽

Author(s):

Linda Ali ◽

Josh P Davis

Keyword(s):

Theoretical Perspective ◽

Emotion Perception ◽

Common Mechanism ◽

Face Identification ◽

Large Individual ◽

Face Memory ◽

Face Identity ◽

Face Matching ◽

Identity Processing ◽

Typical Range

Large individual differences in face identification ability and face emotion perception ability correlate in the neurotypical population. The current study recruited a large sample of participants aged 18-70-years (n = 687). The sample included super-recognisers with exceptional face identity processing skills (n = 74) to determine whether the relationship extends to the top of the ability spectrum. Participants completed one short-term face memory test, a simultaneous face matching test, and two face emotion perception tests. Face memory and face matching scores were moderately positively correlated with each other, and with total scores on the two face emotion perception tests. Super-recognisers outperformed typical-range ability controls at the perception of anger, fear, happy, and neutral, but not disgust or sadness. Identity processing and emotion perception accuracy across the whole sample was influenced by gender and age. From a theoretical perspective, these results demonstrate that correlations found between typical-range ability participants also extend to the top end of the face identification spectrum, implying that a common mechanism may drive performance. Practically, super-recognisers possessing superior face identity processing and emotion perception might enhance the performance of organisations such as police forces or security that rely on these attributes in their staff.

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Measuring Face Recognition Performance: Investigating Cambridge Face Memory Test and Glasgow Face Matching Test Performances in Groups with Different Face Recognition Experience

Yeni Symposium ◽

10.5455/nys.20200402061836 ◽

2020 ◽

pp. 1

Author(s):

Aycan Kapucu ◽

Ayşegül Aydınlık ◽

Sonia Amado

Keyword(s):

Face Recognition ◽

Recognition Performance ◽

Memory Test ◽

Face Memory ◽

Face Matching ◽

Matching Test

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Self-reported face recognition abilities for own and other-race faces

10.31234/osf.io/ru973 ◽

2020 ◽

Author(s):

Alejandro Estudillo

Keyword(s):

Face Recognition ◽

Individual Differences ◽

Eyewitness Identification ◽

Memory Test ◽

Objective Measures ◽

Face Identification ◽

Moderate Correlation ◽

Face Memory ◽

Eyewitness Accuracy ◽

Race Effect

PurposeThe other-race effect shows that people are better recognizing faces from their own-race compared to other-race faces. This effect can have dramatic consequences in applied scenarios whereby face identification is paramount, such as eyewitness identification. This study investigates observers’ insights into their ability to recognize own- and other-race faces.Design/methodology/approachChinese ethnic observers performed objective measures of own- and other-race face recognition —the Cambridge Face Memory Test Chinese and the Cambridge Face Memory Test original—, the PI20 —a 20-items self-reported measured of general face recognition abilities—, and the ORE20 —a new developed 20-items self-reported measure of other-race face recognition—. FindingsRecognition of own-race faces was better compared to other-race faces. This effect was also evident at a phenomenological level, as observers reported to be worse recognizing other-race faces compared to own-race faces. Additionally, although a moderate correlation was found between own-race face recognition abilities and the PI20, individual differences in the recognition of other-race faces was only poorly associated with observers’ scores in the ORE20. ImplicationsThese results suggest that observers’ insights to recognize faces are more consistent and reliable for own-race faces. Practical implicationsSelf-reported measures of other-race recognition could produce misleading results. Thus, when evaluating eyewitness’ accuracy identifying other-race faces, objective measures should be employed.

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Voice Identification Using MFCC and Vector Quantization

Baghdad Science Journal ◽

10.21123/bsj.2020.17.3(suppl.).1019 ◽

2020 ◽

Vol 17 (3(Suppl.)) ◽

pp. 1019

Author(s):

Bassel Alkhatib ◽

Mohammad Madian Waleed Kamal Eddin

Keyword(s):

Vector Quantization ◽

Speech Processing ◽

Large Scale ◽

Feature Matching ◽

Speaker Identification ◽

Voice Recognition ◽

Security Systems ◽

Voice Identification ◽

New Methodologies ◽

Quick Search

The speaker identification is one of the fundamental problems in speech processing and voice modeling. The speaker identification applications include authentication in critical security systems and the accuracy of the selection. Large-scale voice recognition applications are a major challenge. Quick search in the speaker database requires fast, modern techniques and relies on artificial intelligence to achieve the desired results from the system. Many efforts are made to achieve this through the establishment of variable-based systems and the development of new methodologies for speaker identification. Speaker identification is the process of recognizing who is speaking using the characteristics extracted from the speech's waves like pitch, tone, and frequency. The speaker's models are created and saved in the system environment and used to verify the identity required by people accessing the systems, which allows access to various services that are controlled by voice, speaker identification involves two main parts: the first part is the feature extraction and the second part is the feature matching.

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The Cambridge Face Memory Test: Results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants

Neuropsychologia ◽

10.1016/j.neuropsychologia.2005.07.001 ◽

2006 ◽

Vol 44 (4) ◽

pp. 576-585 ◽

Cited By ~ 492

Author(s):

Brad Duchaine ◽

Ken Nakayama

Keyword(s):

Memory Test ◽

Test Results ◽

Face Memory ◽

Face Stimuli ◽

Neurologically Intact ◽

Inverted Face

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Pengaruh Perubahan Pitch dan Formant Terhadap Hasil Identifikasi Kepemilikan Suara Dengan Metode Audio Forensik

JTT (Jurnal Teknologi Terpadu) ◽

10.32487/jtt.v9i1.894 ◽

2021 ◽

Vol 9 (1) ◽

pp. 1-8

Author(s):

Mifta Nur Farid ◽

Dani Dwi Putra ◽

Barokatun Hasanah

Keyword(s):

Analysis Of Variance ◽

Voice Recognition ◽

Frequency Characteristics ◽

Sound Recordings ◽

Voice Identification ◽

Recorded Sound ◽

Audio Forensics ◽

The Difference ◽

Pitch Analysis ◽

The Voice

Audio forensics is a field of science that analyzes audio such as sound recordings. Voice recordings always have information in the form of frequency characteristics, the identities of these frequencies can be identified. Furthermore, an analysis of changes in pitch and formant will be carried out. This study used pitch analysis and analysis of variance on formants. With the correct procedure for handling recorded sound evidence which is then followed by procedural examination and analysis, it is hoped that the results of the voice recognition examination can scientifically show the ownership of the voice in the recording. Based on the results of the overall analysis of the sound recordings of evidence and comparison after carrying out various stages of analysis, the voice recordings are "not identical" from the same person. The thing that causes the inequality in voice identification is the difference in intonation or tone of the subject's speech when the voice is recorded.

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