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<dc:title xml:lang="pl"><![CDATA[Investigation of the Lombard effect based on a machine learning approach]]></dc:title>
<dc:creator><![CDATA[Korvel, Gražina]]></dc:creator>
<dc:creator><![CDATA[Treigys, Povilas]]></dc:creator>
<dc:creator><![CDATA[Kąkol, Krzysztof]]></dc:creator>
<dc:creator><![CDATA[Kostek, Bożena]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[Lombard effect]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[speech detection]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[noise signal]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[self-similarity matrix]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[convolutional neural network (CNN)]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[The Lombard effect is an involuntary increase in the speaker`s pitch, intensity, and duration in the presence of noise. It makes it possible to communicate in noisy environments more effectively. This study aims to investigate an efficient method for detecting the Lombard effect in uttered speech. The influence of interfering noise, room type, and the gender of the person on the detection process is examined. First, acoustic parameters related to speech changes produced by the Lombard effect are extracted. Mid-term statistics are built upon the parameters and used for the self-similarity matrix construction.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[They constitute input data for a convolutional neural network (CNN). The self-similarity-based approach is then compared with two other methods, i.e., spectrograms used as input to the CNN and speech acoustic parameters combined with the k-nearest neighbors algorithm. The experimental investigations show the superiority of the self-similarity approach applied to Lombard effect detection over the other two methods utilized. Moreover, small standard deviation values for the self-similarity approach prove the resulting high accuracies.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Foryś, Urszula - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Rejniak, Katarzyna - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Pękala, Barbara - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Bartłomiejczyk, Agnieszka - ed.]]></dc:contributor>
<dc:date><![CDATA[2023]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/86685/AMCS_2023_33_3_11.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/101614/edition/86685/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:86685]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 33, number 3 (2023)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=129]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:101614]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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