<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>3447</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>195</pageFirst>
    <pageLast>198</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Association for Computing Machinery</publisherName>
    <publisherPlace>New York, NY, United States</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Prosodic addressee-detection : ensuring privacy in always-on spoken dialog systems</title>
    <abstract language="eng">We analyze the addressee detection task for complexity-identical dialog for both human conversation and device-directed speech. Our recurrent neural model performs at least as good as humans, who have problems with this task, even native speakers, who profit from the relevant linguistic skills. We perform ablation experiments on the features used by our model and show that fundamental frequency variation is the single most relevant feature class. Therefore, we conclude that future systems can detect whether they are addressed based only on speech prosody which does not (or only to a very limited extent) reveal the content of conversations not intended for the system.</abstract>
    <parentTitle language="eng">Tagungsband Mensch und Computer (MuC'20): 06.09.2020 - 09.09.2020, Magdeburg</parentTitle>
    <identifier type="isbn">9781450375405</identifier>
    <identifier type="doi">10.1145/3404983.3410021</identifier>
    <enrichment key="opus.import.date">2022-04-09T13:25:33+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Timo Baumann</author>
    <author>Ingo Siegert</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>addressee detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>complexity-identical human-computer interaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>computational paralinguistics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fundamental frequency variation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>recurrent neural network</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16315">Information und Kommunikation</collection>
  </doc>
</export-example>
