<?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>1390-6542</journal-id>
<journal-title><![CDATA[Enfoque UTE]]></journal-title>
<abbrev-journal-title><![CDATA[Enfoque UTE]]></abbrev-journal-title>
<issn>1390-6542</issn>
<publisher>
<publisher-name><![CDATA[Universidad UTE]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1390-65422022000300045</article-id>
<article-id pub-id-type="doi">10.29019/enfoqueute.839</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Reconocimiento del habla con acento español basado en un modelo acústico]]></article-title>
<article-title xml:lang="en"><![CDATA[Speech recognition based on Spanish accent acoustic model]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Plaza]]></surname>
<given-names><![CDATA[Johanna]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez-Zhunio]]></surname>
<given-names><![CDATA[Cristina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Acosta-Urigüen]]></surname>
<given-names><![CDATA[María-Inés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Orellana]]></surname>
<given-names><![CDATA[Marcos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cedillo]]></surname>
<given-names><![CDATA[Priscila]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cedillo]]></surname>
<given-names><![CDATA[Priscila]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zambrano-Martinez]]></surname>
<given-names><![CDATA[Jorge Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Azuay  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Azuay  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad del Azuay  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad del Azuay  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad del Azuay  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af6">
<institution><![CDATA[,Universidad de Cuenca  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af7">
<institution><![CDATA[,Universidad del Azuay  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<volume>13</volume>
<numero>3</numero>
<fpage>45</fpage>
<lpage>57</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S1390-65422022000300045&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S1390-65422022000300045&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S1390-65422022000300045&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El objetivo de este artículo fue generar un modelo reconocimiento automático de voz (RAV) basado en la traducción de la voz humana a texto, a este proceso se lo ha considerado como una de las ramas de la inteligencia artificial. El análisis de voz permite identificar información sobre la acústica, fonética, sintáctica, semántica de las palabras, entre otros elementos que pueden identificar ambigüedad en términos, errores de pronunciación, sintáctica similar pero semántica diferente, que representan características propias del lenguaje humano. El modelo se centró en el análisis acústico de las palabras, proponiendo la generación de una metodología para reconocimiento acústico, a partir de transcripciones del habla de audios que contienen voz humana. Se utilizó la tasa de error por palabra para identificar la precisión del modelo. Los audios son llamadas de emergencia registrados por el Servicio Integrado de Seguridad ECU 911. El modelo fue entrenado con la herramienta CMUSphinx para idioma español sin conexión a internet. Los resultados mostraron que la tasa de error por palabra varía en relación con la cantidad de audios; es decir a mayor cantidad de audios menor cantidad de palabras erróneas y mayor exactitud del modelo. La investigación concluyó haciendo énfasis en la duración de cada audio como variable que afecta la precisión del modelo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The objective of the article was to generate an Automatic Speech Recognition (ASR) model based on the translation from human voice to text, being considered as one of the branches of artificial intelligence. Voice analysis allows identifying information about the acoustics, phonetics, syntax, semantics of words, among other elements where ambiguity in terms, pronunciation errors, similar syntax but different semantics can be identified, which represent characteristics of the language. The model focused on the acoustic analysis of words proposing the generation of a methodology for acoustic recognition from speech transcripts from audios containing human voice and the error rate per word was considered to identify the accuracy of the model. The audios were taken from the Integrated Security Service ECU 911 that represent emergency calls registered by the entity. The model was trained with the CMUSphinx tool for the Spanish language without internet connection. The results showed that the word error rate varies in relation to the number of audios; that is, the greater the number of audios, the smaller number of erroneous words and the greater the accuracy of the model. The investigation concluded by emphasizing the duration of each audio as a variable that affects the accuracy of the model.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[RAV]]></kwd>
<kwd lng="es"><![CDATA[Modelo de Lenguaje]]></kwd>
<kwd lng="es"><![CDATA[CMUSphinx]]></kwd>
<kwd lng="en"><![CDATA[Automatic Speech Recognition]]></kwd>
<kwd lng="en"><![CDATA[Language Model]]></kwd>
<kwd lng="en"><![CDATA[CMUSphinx]]></kwd>
</kwd-group>
</article-meta>
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