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                <text>Conference Papers</text>
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    <name>Conference Paper</name>
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              <text>Improving Speaker Gender Detection by Combining Pitch and SDC</text>
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              <text>Datasets; Gender detection; MFCC; Pitch; SDC; XGBoost</text>
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              <text>Gender detection is helpful in various applications, such as speaker and emotion recognition, which helps with online learning, telecom caller identification, etc. This process is also used in speech analysis and initiating human-machine interaction. Gender detection is a complex process but an essential part of the digital world dealing with voice. The proposed approach is to detect gender from a speech by combining acoustic features like shifted delta cepstral (SDC) and pitch. The first step is preprocessing the speech sample to retrieve valid speech data. The second step is to calculate the pitch and SDC for each frame. The multifeature fusion method combines the speech features, and the XGBoost model is applied to detect gender. This approach results in accuracy rates of 99.44 and 99.37% with the help of RAVDESS and TIMIT datasets compared to the pre-defined methods.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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          <name>Creator</name>
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              <text>Mohanty A.; Cherukuri R.C.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-818, pp. 451-462.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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          <name>Date</name>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-99-7862-5_34" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-99-7862-5_34&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187721881&amp;amp;doi=10.1007%2F978-981-99-7862-5_34&amp;amp;partnerID=40&amp;amp;md5=b92ed4600ed3204cbbff7df0f854fa38" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187721881&amp;amp;doi=10.1007%2f978-981-99-7862-5_34&amp;amp;partnerID=40&amp;amp;md5=b92ed4600ed3204cbbff7df0f854fa38&lt;/a&gt;</text>
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            <elementText elementTextId="168994">
              <text>Restricted Access</text>
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              <text>ISSN: 23673370; ISBN: 978-981997861-8</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Mohanty A., CHRIST (Deemed to be University), Karnataka, Bangalore, India; Cherukuri R.C., CHRIST (Deemed to be University), Karnataka, Bangalore, India</text>
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