Mechanisms for Profiling

Singh, Rita

doi:10.1007/978-981-13-8403-5_8

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…The use of biometric data, in fact, relies on the assumption that data can disclose truths about the self (Amoore, 2020; Humphrey, 2022). This assumption is at the base of forensic practices such as voice profiling (Singh, 2019) – the possibility of recognizing people’s identity, emotions and intentions from the measurement of their voice features. These practices, increasingly common in everyday online activities, assume transparency (Amoore, 2020) as an unquestioned value and produce accountable subjects through computational regimes of visibility and intelligibility (Ajana, 2013).…”

Section: Google’s Euphonia and The Ethopolitics Of Disabilitymentioning

confidence: 99%

Ethopolitical media: Organizing Assistive Technology, disability and care in the platform society

Napolitano,

Sicca

2024

Media, Culture & Society

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In this paper we prompt a re-reading of Assistive Technology (AT) as a media system that organizes disability in the framework of digital health-care and the platform society. Drawing on disability media studies and organization studies, we investigate how the arrival of big tech and digital platforms in the field of AT reconfigures ways to account for, classify and potentially discriminate against disability. We argue that this new configuration can be explained as a shift from a biopolitical model – oriented toward disability normalization – to an ethopolitical model, oriented toward optimization and health enhancement. In the conclusions, we put forward the concept of ethopolitical media and discuss the implications of this for wider debates in media and cultural studies which deal with the relationship between media, health, and self-care.

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Section: Google’s Euphonia and The Ethopolitics Of Disabilitymentioning

confidence: 99%

Ethopolitical media: Organizing Assistive Technology, disability and care in the platform society

Napolitano,

Sicca

2024

Media, Culture & Society

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“…Auditory nonverbal disclosures are noisy signals, and thus inferences inherently fall short of precision, both when made by humans (Juslin and Scherer 2005) and when made by algorithms (Kröger, Lutz and Raschke 2020). Yet, auditory signal analysis is a highly active field of research, in which both the scope of investigated phenomena and performance accuracy keep increasing 5 (for technical reviews of the state of the art, see, e.g., Bai et al [2020], Chaki [2021], Cummins and Schuller [2019], Schuller et al [2021], and Singh [2019]). All computational approaches referenced in Table 2 perform above chance level; in certain domains, algorithmic inferences based on auditory nonverbal disclosures are already as accurate as, and sometimes even more accurate than, human judgments (Cummins, Baird, and Schuller 2018; Kröger, Lutz, and Raschke 2020).…”

Section: Nonverbal Disclosure In Oral Versus Manual Interactions With...mentioning

confidence: 99%

Information Disclosure in the Era of Voice Technology

Melzner¹,

Bonezzi²,

Meyvis³

2023

Journal of Marketing

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Whenever consumers interact with technological devices connected to the internet, they disclose information about themselves. The rapid diffusion of voice technology is shifting the way consumers interact with technological devices from typing or clicking to speaking. This article offers a comprehensive analysis of how this occurring shift from manual to oral communication with technology affects information disclosure. It first considers verbal disclosure and provides a conceptual framework that explicates how voice technology can influence consumers’ propensity to reveal information about themselves through semantic content disclosed voluntarily. It then considers non-verbal disclosure and provides an analysis of how voice technology enables the collection of information revealed rather unintentionally through vocal paralanguage and ambient sound. The article offers testable propositions and poses open research questions that can serve as impetus for future research. Additionally, it provides insights to marketers for how to navigate voice technology as a source of consumer information and to policymakers for how to better protect consumer privacy in interactions with voice technology.

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“…The working hypothesis for this paper, and one that has also recently been proposed in the context of voice profiling [ 1 ], is that if a given factor exerts an influence on the speaker, and if pathways of biological effects can be traced from that influence to the speaker’s voice production system, then voice must be affected (and must carry biomarkers for the factor). The methodology proposed herein is a literal test of this hypothesis in that it traces biological pathways from cause to effect to establish the existence of biomarkers.…”

Section: Introductionmentioning

confidence: 99%

A Gene-Based Algorithm for Identifying Factors That May Affect a Speaker’s Voice

Singh

2023

Entropy

Self Cite

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Over the past decades, many machine-learning- and artificial-intelligence-based technologies have been created to deduce biometric or bio-relevant parameters of speakers from their voice. These voice profiling technologies have targeted a wide range of parameters, from diseases to environmental factors, based largely on the fact that they are known to influence voice. Recently, some have also explored the prediction of parameters whose influence on voice is not easily observable through data-opportunistic biomarker discovery techniques. However, given the enormous range of factors that can possibly influence voice, more informed methods for selecting those that may be potentially deducible from voice are needed. To this end, this paper proposes a simple path-finding algorithm that attempts to find links between vocal characteristics and perturbing factors using cytogenetic and genomic data. The links represent reasonable selection criteria for use by computational by profiling technologies only, and are not intended to establish any unknown biological facts. The proposed algorithm is validated using a simple example from medical literature—that of the clinically observed effects of specific chromosomal microdeletion syndromes on the vocal characteristics of affected people. In this example, the algorithm attempts to link the genes involved in these syndromes to a single example gene (FOXP2) that is known to play a broad role in voice production. We show that in cases where strong links are exposed, vocal characteristics of the patients are indeed reported to be correspondingly affected. Validation experiments and subsequent analyses confirm that the methodology could be potentially useful in predicting the existence of vocal signatures in naïve cases where their existence has not been otherwise observed.

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Mechanisms for Profiling

Cited by 3 publications

References 29 publications

Ethopolitical media: Organizing Assistive Technology, disability and care in the platform society

Ethopolitical media: Organizing Assistive Technology, disability and care in the platform society

Information Disclosure in the Era of Voice Technology

A Gene-Based Algorithm for Identifying Factors That May Affect a Speaker’s Voice

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