The Contribution of Speech Rhythm and Pitch to Speaker Recognition

Dommelen, Wim A. van

doi:10.1177/002383098703000403

Cited by 31 publications

(27 citation statements)

References 13 publications

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“…There were also significant negative correlations between correct Twin B identification scores and Twin B's corresponding FO onset, FO SD, and FO range values (see Table 3). Perhaps fundamental frequency may have played some role in this speaker-identification task, a suggestion that is supported by previous studes which have also reported on the contribution of FO to speaker-identification (Abberton & Fourcin, 1978;Van Dommelen, 1987, 1990. These results suggest that identifications of the hvins from the pure syllables were higher with lower FO parameter values.…”

Section: Discussionsupporting

confidence: 80%

Identification of Twins from Pure (Single Speaker) and Hybrid (Fused) Syllables: An Acoustic and Perceptual Case Study

Whiteside

Rixon

2000

Percept Mot Skills

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This study describes a speaker-identification task carried out using two sets of single monosyllables and two sets of hybrid monosyllables prepared from monosyllabic words spoken by a set of monozygotic twins. The task was carried out by 6 listeners who were all very familiar with the twins and by the twins themselves. The identification scores for the pure syllables showed that both twins were identified with accuracy above chance; however, the percentage scores were less than perfect in both cases (71.4% and 72.9%). Analysis of the identification scores for the hybrid syllables showed that listeners had difficulty in attributing the hybrid syllables to a single twin. This was confirmed by the lack of significance in three binomial tests. The pure syllables were analysed acoustically to obtain a set of acoustic parameters for each twin. The results of the perceptual study are discussed with reference to the acoustic parameters. In addition, the implications of the study for issues in speaker identification are considered.

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Section: Discussionsupporting

confidence: 80%

Identification of Twins from Pure (Single Speaker) and Hybrid (Fused) Syllables: An Acoustic and Perceptual Case Study

Whiteside

Rixon

2000

Percept Mot Skills

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“…From a speech production perspective, a speaker’s fundamental frequency relates to the rate of vocal fold vibration (known as glottal pulse rate), and formant spacing is affected by the length and shape of the vocal tract—which are relatively fixed for a speaker, although can be modified slightly by changing the positions of the articulators, such as the tongue and lips. Previous research demonstrates that listeners use both fundamental frequency and speech formants to judge the identity of people who are familiar ( LaRiviere, 1975 ; Abberton and Fourcin, 1978 ; Van Dommelen, 1987 , Van Dommelen, 1990 ; Lavner et al., 2000 ; Lavner et al., 2001 ; Holmes et al., 2018a ) and unfamiliar ( Matsumoto et al., 1973 ; Walden et al., 1978 ; Murry and Singh, 1980 ; Baumann and Belin, 2009 ; Gaudrain et al., 2009 ). To extend the current model to recognise voices, the next step is to specify how combinations of fundamental and formant frequencies are used to infer speaker identity.…”

Section: Discussionmentioning

confidence: 99%

Active listening

et al. 2021

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This paper introduces active listening, as a unified framework for synthesising and recognising speech. The notion of active listening inherits from active inference, which considers perception and action under one universal imperative: to maximise the evidence for our (generative) models of the world. First, we describe a generative model of spoken words that simulates (i) how discrete lexical, prosodic, and speaker attributes give rise to continuous acoustic signals; and conversely (ii) how continuous acoustic signals are recognised as words. The ‘active’ aspect involves (covertly) segmenting spoken sentences and borrows ideas from active vision. It casts speech segmentation as the selection of internal actions, corresponding to the placement of word boundaries. Practically, word boundaries are selected that maximise the evidence for an internal model of how individual words are generated. We establish face validity by simulating speech recognition and showing how the inferred content of a sentence depends on prior beliefs and background noise. Finally, we consider predictive validity by associating neuronal or physiological responses, such as the mismatch negativity and P300, with belief updating under active listening, which is greatest in the absence of accurate prior beliefs about what will be heard next.

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“…It has been established that spectral information of vocalizations can be used for identification by nonhuman primates (Rendall et al., ; Weiss et al., ), and our study showed that fine variations in pitch of the order of semitones can be distinguished and represented by A1 neurons. It has also been reported that voice pitch can be used for speaker identification in humans (Dommelen, ; Gelfer & Mikos, ; Walker, Bizley, King, & Schnupp, ), and for individual identification in other nonhuman primates (Zoloth et al., ; Kojima et al., ) and birds (Nelson, ). The current study provides a foundation for future work studying the neural circuits involved voice discrimination in awake behaving animals, which will undoubtedly include other areas in addition to A1.…”

Section: Discussionmentioning

confidence: 99%

Distributed representation of vocalization pitch in marmoset primary auditory cortex

Zhu

Allitt

Samuel

et al. 2018

Eur J of Neuroscience

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The pitch of vocalizations is a key communication feature aiding recognition of individuals and separating sound sources in complex acoustic environments. The neural representation of the pitch of periodic sounds is well defined. However, many natural sounds, like complex vocalizations, contain rich, aperiodic or not strictly periodic frequency content and/or include high‐frequency components, but still evoke a strong sense of pitch. Indeed, such sounds are the rule, not the exception but the cortical mechanisms for encoding pitch of such sounds are unknown. We investigated how neurons in the high‐frequency representation of primary auditory cortex (A1) of marmosets encoded changes in pitch of four natural vocalizations, two centred around a dominant frequency similar to the neuron's best sensitivity and two around a much lower dominant frequency. Pitch was varied over a fine range that can be used by marmosets to differentiate individuals. The responses of most high‐frequency A1 neurons were sensitive to pitch changes in all four vocalizations, with a smaller proportion of the neurons showing pitch‐insensitive responses. Classically defined excitatory drive, from the neuron's monaural frequency response area, predicted responses to changes in vocalization pitch in <30% of neurons suggesting most pitch tuning observed is not simple frequency‐level response. Moreover, 39% of A1 neurons showed call‐invariant tuning of pitch. These results suggest that distributed activity across A1 can represent the pitch of natural sounds over a fine, functionally relevant range, and exhibits pitch tuning for vocalizations within and outside the classical neural tuning area.

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The Contribution of Speech Rhythm and Pitch to Speaker Recognition

Cited by 31 publications

References 13 publications

Identification of Twins from Pure (Single Speaker) and Hybrid (Fused) Syllables: An Acoustic and Perceptual Case Study

Identification of Twins from Pure (Single Speaker) and Hybrid (Fused) Syllables: An Acoustic and Perceptual Case Study

Active listening

Distributed representation of vocalization pitch in marmoset primary auditory cortex

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