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  <controlfield tag="008">210629s9999    xx            000 0 und d</controlfield>
  <datafield tag="022" ind1=" " ind2=" ">
    <subfield code="a">1708-8186</subfield>
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  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="a">Kishore Tanniru </subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2="0">
    <subfield code="a">Development of equally intelligible Telugu sentence-lists to test speech recognition in noise</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2017</subfield>
  </datafield>
  <datafield tag="300" ind1=" " ind2=" ">
    <subfield code="a">664-671</subfield>
  </datafield>
  <datafield tag="490" ind1=" " ind2=" ">
    <subfield code="v">Volume 56  Issue 9</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">Objective: To develop sentence lists in the Telugu language for the assessment of speech recognition threshold (SRT) in the presence of background noise through identification of the mean signal-to-noise ratio required to attain a 50% sentence recognition score (SRTn). Design: This study was conducted in three phases. The first phase involved the selection and recording of Telugu sentences. In the second phase, 20 lists, each consisting of 10 sentences with equal intelligibility, were formulated using a numerical optimisation procedure. In the third phase, the SRTn of the developed lists was estimated using adaptive procedures on individuals with normal hearing. Study sample: A total of 68 native Telugu speakers with normal hearing participated in the study. Of these, 18 (including the speakers) performed on various subjective measures in first phase, 20 performed on sentence/word recognition in noise for second phase and 30 participated in the list equivalency procedures in third phase. Results: In all, 15 lists of comparable difficulty were formulated as test material. The mean SRTn across these lists corresponded to &#x2212;2.74 (SD&#x2009;=&#x2009;0.21). Conclusions: The developed sentence lists provided a valid and reliable tool to measure SRTn in Telugu native speakers.</subfield>
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  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Speech recognition, background noise, sentence lists, Telugu, speech perception</subfield>
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  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Anusha, K</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Chandni Jain </subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Niraj Kumar Singh </subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Ramadevi Sreenivas K. J. </subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Sreeraj Konadath </subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Vijaya Kumar Narne </subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="a">International Journal of Audiology </subfield>
  </datafield>
  <datafield tag="856" ind1=" " ind2=" ">
    <subfield code="u">https://doi.org/10.1080/14992027.2017.1307530</subfield>
  </datafield>
  <datafield tag="942" ind1=" " ind2=" ">
    <subfield code="c">FJAS</subfield>
    <subfield code="y">FJAS</subfield>
  </datafield>
  <datafield tag="999" ind1=" " ind2=" ">
    <subfield code="c">17847</subfield>
    <subfield code="d">17847</subfield>
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