Speaker-individuality in suprasegmental temporal features: Implications for forensic voice comparison

Leemann, Adrian; Kolly, Marie-José; Dellwo, Volker

doi:10.1016/j.forsciint.2014.02.019

Cited by 45 publications

(73 citation statements)

References 35 publications

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“…The TEVOID corpus 5,6 was used for the present study. It contains 16 native speakers of Z€ urich German (8 female, 8 male; mean age ¼ 27, age standard deviation ¼ 3.6, age range ¼ 20-33, no reported speech and hearing disorders).…”

Section: Corpusmentioning

confidence: 99%

“…Syllable boundaries were annotated automatically based on segment sonority rules; sonority scales were manually attributed to each segment type. 5,6 Mm. 1.…”

Section: Corpusmentioning

confidence: 99%

“…17 It was demonstrated to be particularly suitable for summarizing the sequential variability in speech over the course of an entire utterance. [5][6][7]17 We notated these measures as MEAN_ The intensity contour (lower plot) was calculated from the speech waveform (upper plot). The amplitude envelope (superimposed over the waveform in the upper plot) was used to facilitate locating the peak and trough points (t P and t T ).…”

Section: Measurement Of Intensity Dynamicsmentioning

confidence: 99%

“…[5][6][7] The rationale of these studies is that articulatory movements are comparable to other domains of human movements (e.g., gait and typing) where individual differences are conspicuous. 3,[5][6][7] Such individualities are related to both individual neurological dispositions, which constrain the motor control over the respective body parts, 8 and ontogenetic anatomical characteristics of moving body parts, which shape their biomechanical properties. 9 As a specialized domain of human motor behavior, articulation also reflects speaker individualities because of anatomical idiosyncrasies of the articulators and the way speakers acquired control over them.…”

Section: Introductionmentioning

confidence: 99%

“…These result in speaker-specific articulatory kinematics, including velocity, acceleration and spatial displacement. 10,11 Such kinematic characteristics are assumed to be the reason for speakerspecific production of prosodic duration 5,6 and intensity variabilities. 7,12 The present research underlies the assumption that the intensity contour shape might be closely related to the articulatory movements responsible for the changes of mouth opening area in an utterance.…”

Section: Introductionmentioning

confidence: 99%

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Between-speaker variability in temporal organizations of intensity contours

Dellwo

2017

The Journal of the Acoustical Society of America

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Intensity contours of speech signals were sub-divided into positive and negative dynamics. Positive dynamics were defined as the speed of increases in intensity from amplitude troughs to subsequent peaks, and negative dynamics as the speed of decreases in intensity from peaks to troughs. Mean, standard deviation, and sequential variability were measured for both dynamics in each sentence. Analyses showed that measures of both dynamics were separately classified and between-speaker variability was largely explained by measures of negative dynamics. This suggests that parts of the signal where intensity decreases from syllable peaks are more speaker-specific. Idiosyncratic articulation may explain such results. Abstract: Intensity contours of speech signals were sub-divided into positive and negative dynamics. Positive dynamics were defined as the speed of increases in intensity from amplitude troughs to subsequent peaks, and negative dynamics as the speed of decreases in intensity from peaks to troughs. Mean, standard deviation, and sequential variability were measured for both dynamics in each sentence. Analyses showed that measures of both dynamics were separately classified and betweenspeaker variability was largely explained by measures of negative dynamics. This suggests that parts of the signal where intensity decreases from syllable peaks are more speaker-specific. Idiosyncratic articulation may explain such results.

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Section: Corpusmentioning

confidence: 99%

“…Syllable boundaries were annotated automatically based on segment sonority rules; sonority scales were manually attributed to each segment type. 5,6 Mm. 1.…”

Section: Corpusmentioning

confidence: 99%

Section: Measurement Of Intensity Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Between-speaker variability in temporal organizations of intensity contours

Dellwo

2017

The Journal of the Acoustical Society of America

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Forensic voice discrimination by lay listeners: The effect of speech type and background noise on performance

Smith

Baguley

Robson

et al. 2018

Applied Cognitive Psychology

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In forensic settings, lay (nonexpert) listeners may be required to compare voice samples for identity. In two experiments we investigated the effect of background noise and variations in speaking style on performance. In each trial, participants heard two recordings, responded whether the voices belonged to the same person, and provided a confidence rating. In Experiment 1, the first recording featured read speech and the second featured read or spontaneous speech. Both recordings were presented in quiet, or with background noise. Accuracy was highest when recordings featured the same speaking style. In Experiment 2, background noise either occurred in the first or second recording. Accuracy was higher when it occurred in the second. The overall results reveal that both speaking style and background noise can disrupt accuracy. Although there is a relationship between confidence and accuracy in all conditions, it is variable. The forensic implications of these findings are discussed.

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Visual mechanisms for voice‐identity recognition flexibly adjust to auditory noise level

Maguinness

Kriegstein

2021

Human Brain Mapping

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Recognising the identity of voices is a key ingredient of communication. Visual mechanisms support this ability: recognition is better for voices previously learned with their corresponding face (compared to a control condition). This so-called 'face-benefit' is supported by the fusiform face area (FFA), a region sensitive to facial form and identity. Behavioural findings indicate that the face-benefit increases in noisy listening conditions. The neural mechanisms for this increase are unknown. Here, using functional magnetic resonance imaging, we examined responses in face-sensitive regions while participants recognised the identity of auditory-only speakers (previously learned by face) in high (SNR À4 dB) and low (SNR +4 dB) levels of auditory noise. We observed a face-benefit in both noise levels, for most participants (16 of 21). In high-noise, the recognition of face-learned speakers engaged the right posterior superior temporal sulcus motion-sensitive face area (pSTS-mFA), a region implicated in the processing of dynamic facial cues. The face-benefit in high-noise also correlated positively with increased functional connectivity between this region and voice-sensitive regions in the temporal lobe in the group of 16 participants with a behavioural face-benefit. In low-noise, the face-benefit was robustly associated with increased responses in the FFA and to a lesser extent the right pSTS-mFA. The findings highlight the remarkably adaptive nature of the visual network supporting voiceidentity recognition in auditory-only listening conditions.

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Speaker-individuality in suprasegmental temporal features: Implications for forensic voice comparison

Cited by 45 publications

References 35 publications

Between-speaker variability in temporal organizations of intensity contours

Between-speaker variability in temporal organizations of intensity contours

Forensic voice discrimination by lay listeners: The effect of speech type and background noise on performance

Visual mechanisms for voice‐identity recognition flexibly adjust to auditory noise level

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