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<p><strong>About</strong></p>
<p>In human communication, speech is a medium to impart and
exchange information. Speech encodes a wealth of information
on, e.g.: socio-economic background; racial, ethnic, and
geographical origin; religious, political, and philosophical
beliefs, health and emotion condition; identity; gender, and
age. All of which is recognisable by humans. With speech
technology evolving, machines are increasingly able to
perceive and process this information. In certain usage
scenarios, this specific information is needed to guarantee
functionality and a proper service. But in many usage
scenarios, this information is not needed and should be
neglected.</p>
<p>For instance, detecting early signs of Parkinson’s disease,
depression/suicide risk, and dementia help physicians sustain
the health of a person by comprehensive medical profiling. In
other circumstances, such profiling, while possible, is
regarded as morally wrong indicating the importance of privacy
in the context of speech analysis. Therefore, the terms
Personally Identifiable Information (PII) and Public Sector
Information (PSI) are used. [Through PII as information
representation, the identity of an individual can be
reasonably inferred directly or indirectly. Information
produced by public entities is PSI.] Regarding privacy in
speech communication, we need not only to arrive at a better
understanding of when speech data is seen as PII or PSI but
also to which extent PII is needed for an application - and
when we want to refrain from it.</p>
<p>We, furthermore, need to secure epistemic value across
disciplines to arrive at a mannerism that is credible and
adequate when designing speech & language technology (SLT)
that respects fundamental human rights throughout product and
service development, implementation, and integration.
Moreover, the interoperability of privacy and security needs
to be better understood. Security is used to countermeasure
subversive users, such as detecting fake audio data in online
banking using voice biometrics and call-center fraud
prevention. Privacy is used to protect individuals from harm,
e.g., through unbeknownst linkage of their (speech) data
resulting in subsequent harm when harvested by adversaries. In
the age of virtual voice assistants, speech communication is
intimately related to daily life in digital societies, and so
are security and privacy matters as well. Consequently, we
need to also elaborate on how to involve the users and their
perspectives on questions of privacy and security.</p>
<p>This special issue seeks to provide a venue for ongoing
research in the recently formed research community on Security
& Privacy in Speech Communication (SPSC), where views of
technological and humanities communities nurture one another
to develop multidisciplinary and interdisciplinary skills.</p>
<p>Technology and research need to aid individuals and society
for which scientific inquiry needs to channel durable
solutions. Currently, academic disciplines exist in silos;
fields need to be bridged, and real-world problems to be
solved. Ways to achieve this can be many in the anticipation
of currently lacking multidisciplinary and interdisciplinary
skill sets, e.g.: concise summaries of developments within a
field for their relation to SLTs (hypothetical or existing);
portrayals of community-driven efforts fostering security
and/or privacy in SLTs; insights into the users’ ideas of the
concepts and its effects on their usage; syntheses and
analyses of SLT systems and continuous holistic improvement of
their design, and security & privacy solutions for SLT
applications including user experience(s).<br>
<br>
The topics of interest for the special issue include, but are
not limited to:</p>
<ul>
<li>Sustainability and Economics</li>
<li>Secure/Private Speech Communication</li>
<li>Secure Computation and Cryptography</li>
<li>Psychology, e.g. media psychology and social psychology</li>
<li>Privacy-preserving Human-Computer Interfaces</li>
<li>Policy and Governance, Law, and Ethics</li>
<li>Natural Language Processing</li>
<li>Machine Learning for secure and private speech processing</li>
<li>Law Enforcement and Forensics</li>
<li>Information and Communication Technology for secure and
private speech processing</li>
<li>Human-Computer Interaction Perspective on Security and
Privacy</li>
<li>Digital Humanities and Anthropology regarding privacy and
security needs</li>
</ul>
<p><strong>Important Dates</strong><br>
Submission deadline: 27 May 2022<br>
<br>
<strong>Lead Guest Editor</strong><br>
Andreas Nautsch, <em>ISCA SIG SPSC, Germany</em> and Rodrigo
Capobianco Guido, <em>São Paulo State University, Brazil,</em><br>
Emails: <a class="moz-txt-link-abbreviated" href="mailto:guido@ieee.org">guido@ieee.org</a><br>
<br>
<strong>Guest Editors</strong><br>
Astrid Carolus, Julius-Maximilians University Würzburg,
Germany<br>
Hemant A. Patil, DA-IICT, India<br>
Ingo Siegert, Otto-von-Guericke Universität Magdeburg, Germany</p>
<p><strong>Important</strong>: Authors should select "<em>Security
& Privacy in Speech Communication</em>" when they reach
the “Article Type” step in the submission system.</p>
<p><strong>Submission instruction</strong>: The submission
system will open in early January 2022. Submissions deemed
suitable will be reviewed by experts in different disciplines.
Published articles will appear in different regular issues of
the journal (clearly marked and branded as Special Issue
articles. Please use IEEE style for citations; footnotes are
neat for brief clarifications, and the layout should support
reading (double column is a suggestion only). Write what
prevails, make it digestible, and palate it to your audience
of inquiry (impart knowledge, empower understanding, and take
care of the willing reader to mature).</p>
<p><strong>Submisison guidelines</strong>: <a
href="https://asmp-eurasipjournals.springeropen.com/submission-guidelines"
class="moz-txt-link-freetext">https://asmp-eurasipjournals.springeropen.com/submission-guidelines
</a></p>
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