1 Ocak 2019 Konferans bildirisi Açık Erişim

Iheme, Leonardo O.; Ozan, Sukru; Akagunduz, Erdem

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/67391</identifier>
  <creators>
    <creator>
      <creatorName>Iheme, Leonardo O.</creatorName>
      <givenName>Leonardo O.</givenName>
      <familyName>Iheme</familyName>
      <affiliation>AdresGezgini Inc, Res &amp; Dev Ctr, Izmir, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Ozan, Sukru</creatorName>
      <givenName>Sukru</givenName>
      <familyName>Ozan</familyName>
      <affiliation>AdresGezgini Inc, Res &amp; Dev Ctr, Izmir, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Akagunduz, Erdem</creatorName>
      <givenName>Erdem</givenName>
      <familyName>Akagunduz</familyName>
      <affiliation>Cankaya Univ, Elect &amp; Elect Engn Dept, Ankara, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Machine Learning-Based Silence Detection In Call Center Telephone Conversations</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/67391</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.67390</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.67391</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">This study presents the development of a voice activity detection (VAD) system tested on call center telephony data obtained from our local site. The concept of bag of audio words (BoAW) combined with a naive Bayes classifier was applied to achieve the task. It was formulated as a binary classification problem with speech as the positive class and silence/background noise as the negative class. All the processing was performed on the Mel-frequency cepstral coefficients (MFCCs) extracted from the audio recordings. The results which are presented as accuracy score and receiver operating characteristics (ROC) indicate an excellent performance of the developed model. The system is to be deployed within our call center to aid data analysis and improve overall efficiency of the center.</description>
  </descriptions>
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