<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1646-9895</journal-id>
<journal-title><![CDATA[RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação]]></journal-title>
<abbrev-journal-title><![CDATA[RISTI]]></abbrev-journal-title>
<issn>1646-9895</issn>
<publisher>
<publisher-name><![CDATA[AISTI - Associação Ibérica de Sistemas e Tecnologias de Informação]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1646-98952025000300036</article-id>
<article-id pub-id-type="doi">10.17013/risti.59.36-52</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Clasificación de Frases en Zapoteco mediante CNN con Espectrogramas]]></article-title>
<article-title xml:lang="en"><![CDATA[Application of CNN in Audio Recognition of Isthmus Zapotec]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Patiño]]></surname>
<given-names><![CDATA[Mariano Martínez]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vázquez]]></surname>
<given-names><![CDATA[Sergio Juárez]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Reyes]]></surname>
<given-names><![CDATA[Efraín Dueñas]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sampedro]]></surname>
<given-names><![CDATA[Francisco Javier Sol]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruiz]]></surname>
<given-names><![CDATA[Nicolas Hernández]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Istmo  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2025</year>
</pub-date>
<numero>59</numero>
<fpage>36</fpage>
<lpage>52</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S1646-98952025000300036&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S1646-98952025000300036&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S1646-98952025000300036&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este estudio se enfoca en la clasificación de espectrogramas, representaciones visuales del audio para aplicar aprendizaje automático. Los métodos tradicionales, como los MFCCs con clasificadores clásicos, presentan limitaciones en lenguas con pocos recursos, como el zapoteco del Istmo. Modelos avanzados como RNNs y transformers requieren grandes volúmenes de datos, difíciles de obtener en contextos indígenas. Como alternativa, se propone una red neuronal convolucional profunda de 28 capas, entrenada con 10 frases comunes convertidas en espectrogramas y aumentadas artificialmente. El modelo logró un 100% de precisión en entrenamiento y 96.2% en validación. Aunque prometedor, se destaca la necesidad de ampliar el conjunto de datos. El trabajo evidencia el potencial del aprendizaje profundo para mejorar la comunicación intercultural y preservar lenguas indígenas en peligro.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This study focuses on the classification of spectrograms, visual representations of audio, for the application of machine learning. Traditional methods, such as MFCCs with classical classifiers, have limitations in resource-poor languages &#8203;&#8203;such as Isthmus Zapotec. Advanced models, such as RNNs and transformers, require large volumes of data, which are often difficult to obtain in indigenous contexts. As an alternative, a 28-layer deep convolutional neural network is proposed, trained with 10 common phrases converted into spectrograms and artificially augmented. The model achieved 100% training accuracy and 96.2% validation accuracy. Although promising, the need to expand the dataset is highlighted. This work demonstrates the potential of deep learning to improve intercultural communication and preserve endangered indigenous languages.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Comunicación intercultural]]></kwd>
<kwd lng="es"><![CDATA[lenguas indígenas zapoteca]]></kwd>
<kwd lng="es"><![CDATA[imágenes espectrales]]></kwd>
<kwd lng="es"><![CDATA[red neuronal profunda]]></kwd>
<kwd lng="en"><![CDATA[Intercultural communication]]></kwd>
<kwd lng="en"><![CDATA[zapotec indigenous languages]]></kwd>
<kwd lng="en"><![CDATA[spectral images]]></kwd>
<kwd lng="en"><![CDATA[deep neural network]]></kwd>
</kwd-group>
</article-meta>
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