Who owns your voice? Linguistic and legal perspectives on the relationship between vocal distinctiveness and the rights of the individual speaker

Watt, Dominic; Harrison, Peter S.; Cabot-King, Lily

doi:10.1558/ijsll.40571

Cited by 6 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Voice data are unlikely to be the first type of data that come to mind with respect to data protection concerns. [10] discuss the concept of "voice ownership" and how this relates to data protection issues. Within their exploration, they illustrate how it is becoming a very real possibility that voice data could be used in a fraudulent way, particularly given the rise in speaker recognition technologies as access mechanisms for digital accounts (such as online banking).…”

Section: Data Protectionmentioning

confidence: 99%

See 1 more Smart Citation

Voicing concerns: The balance between data protection principles and research developments in forensic speech science

2021

View full text Add to dashboard Cite

Section: Data Protectionmentioning

confidence: 99%

“…Underlying the research efforts in [10] and [11] has been the broader increase in awareness across sectors, and the public, with respect to data protection and privacy. This increase in awareness is reflected in legislation and court rulings.…”

Section: Data Protectionmentioning

confidence: 99%

Voicing concerns: The balance between data protection principles and research developments in forensic speech science

2021

View full text Add to dashboard Cite

“…A decade ago, Yamagishi et al [3] (p. 1) outlined that "it is not easy, and certainly not cost effective, for manufacturers to create personalized synthetic voices". However, technology has sufficiently matured to realistically emulate individual speakers' voices electronically [12]. Researchers have shown that the results of new speech-synthesis systems are good enough to mislead listeners to thinking that they are listening to authentic voices when they are in fact synthetic [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Situational Analysis of Current Speech-Synthesis Systems for Child Voices: A Scoping Review of Qualitative and Quantitative Evidence

et al. 2022

View full text Add to dashboard Cite

(1) Background: Speech synthesis has customarily focused on adult speech, but with the rapid development of speech-synthesis technology, it is now possible to create child voices with a limited amount of child-speech data. This scoping review summarises the evidence base related to developing synthesised speech for children. (2) Method: The included studies were those that were (1) published between 2006 and 2021 and (2) included child participants or voices of children aged between 2–16 years old. (3) Results: 58 studies were identified. They were discussed based on the languages used, the speech-synthesis systems and/or methods used, the speech data used, the intelligibility of the speech and the ages of the voices. Based on the reviewed studies, relative to adult-speech synthesis, developing child-speech synthesis is notably more challenging. Child speech often presents with acoustic variability and articulatory errors. To account for this, researchers have most often attempted to adapt adult-speech models, using a variety of different adaptation techniques. (4) Conclusions: Adapting adult speech has proven successful in child-speech synthesis. It appears that the resulting quality can be improved by training a large amount of pre-selected speech data, aided by a neural-network classifier, to better match the children’s speech. We encourage future research surrounding individualised synthetic speech for children with CCN, with special attention to children who make use of low-resource languages.

show abstract

A segmentally informed solution to automatic accent classification and its advantages to forensic applications

Brown

Franco-Pedroso

González-Rodríguez

2022

The International Journal of Speech, Language and the Law

View full text Add to dashboard Cite

Traditionally, work in automatic accent recognition has followed a similar research trajectory to that of language identification, dialect identification and automatic speaker recognition. The same acoustic modelling approaches that have been implemented in speaker recognition (such as GMM-UBM and i-vector-based systems) have also been applied to automatic accent recognition. These approaches form models of speakers’ accents by taking acoustic features from right across the speech signal without knowledge of its phonetic content. Particularly for accent recognition, however, phonetic information is expected to add substantial value to the task. The current work presents an alternative modelling approach to automatic accent recognition, which forms models of speakers’ pronunciation systems using segmental information. This article claims that such an approach to the problem makes for a more explainable method and therefore is a more appropriate method to deploy in settings where it is important to be able to communicate methods, such as forensic applications. We discuss the issue of explainability and show how the system operates on a large 700-speaker dataset of non-native English conversational telephone recordings.

show abstract

Who owns your voice? Linguistic and legal perspectives on the relationship between vocal distinctiveness and the rights of the individual speaker

Cited by 6 publications

References 44 publications

Voicing concerns: The balance between data protection principles and research developments in forensic speech science

Voicing concerns: The balance between data protection principles and research developments in forensic speech science

A Situational Analysis of Current Speech-Synthesis Systems for Child Voices: A Scoping Review of Qualitative and Quantitative Evidence

A segmentally informed solution to automatic accent classification and its advantages to forensic applications

Contact Info

Product

Resources

About