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    dc.contributor.authorJiménez Moreno, Robinson-
    dc.contributor.authorCastro Pescador, Andrés Mauricio-
    dc.contributor.authorEspitia Cubillos, Anny Astrid-
    dc.date.accessioned2025-05-08T14:55:56Z-
    dc.date.available2025-05-08T14:55:56Z-
    dc.date.issued2025-01-01-
    dc.identifier.citationJiménez Moreno, R., Castro Pescador, A. M., & Espitia Cubillos, A. A. (2025). Aprendizaje profundo para selección de opciones numéricas por voz como herramientas para chatbot. REVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA), 1(45), 74–81. https://doi.org/10.24054/rcta.v1i45.3044es_CO
    dc.identifier.issn1692-7257-
    dc.identifier.issn2500-8625-
    dc.identifier.urihttp://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/9478-
    dc.descriptionEste documento presenta el diseño de un asistente tipo chatbot operado por voz que funciona siguiendo un modelo de dialogo entre usuario y robot, el cual es entrenado con algoritmos de aprendizaje profundo usando una base de datos de espectrogramas, construidos a partir de voces tanto masculinas como femeninas, basados en la transformada de Fourier de corto tiempo y los coeficientes cepstrales de frecuencia Mel como técnicas de preprocesamiento de señales. Para el reconocimiento y clasificación de patrones de voz se diseñan cinco arquitecturas de red convolucional con los mismos parámetros. Se compara el desempeño en el entrenamiento de las redes donde todas obtuvieron grados de exactitud superior al 92.8%, se observa que el número de capas de las redes afecta el número de parámetros de aprendizaje, su grado de exactitud y peso digital, en general mayor cantidad de capas incrementa tanto el tiempo de entrenamiento como el tiempo de clasificación. Finalmente, para su validación mediante un App de chatbot, el diseño de la red seleccionada es aplicado al diligenciamiento de una encuesta que usa una escala de Likert de 1 a 5, en donde los usuarios además de decir la opción seleccionada la confirman con un Sí o un No, la App reproduce el audio de cada pregunta, muestra su identificación, escucha y confirma las respuestas del usuario. Se concluye el diseño de red seleccionado permite desarrollar aplicaciones de chatbot basadas en interacción por audio.es_CO
    dc.description.abstractThis document presents the design of a voice-operated chatbot-type assistant that works following a dialogue model between user and robot, which is trained with deep learning algorithms, using a database of spectrograms constructed from male and female voices, based on the short-time Fourier transform and Mel frequency cepstral coefficients as signal preprocessing techniques. For the recognition and classification of voice patterns, five convolutional network architectures are designed with the same parameters. The performance achieved in the training of the networks is compared, where all degrees of accuracy were greater than 92.8%. It is observed that the number of layers of the networks affects the number of learning parameters, their degree of accuracy and digital weight; in general, a greater number of layers increases both the training time and the classification time. Finally, for validation through a chatbot App, the selected network is applied to the completion of a survey that uses a Likert scale from 1 to 5, where users, in addition to saying the selected option, confirm it with a Yes or No, the App plays the audio of each question, shows its identification, listens and confirms the user's answers. The selected network design is concluded, allowing the development of chatbot applications based on audio interaction.es_CO
    dc.format.extent8es_CO
    dc.format.mimetypeapplication/pdfes_CO
    dc.language.isoeses_CO
    dc.publisherAldo Pardo García, Revista Colombiana de Tecnologías de Avanzada, Universidad de Pamplona.es_CO
    dc.relation.ispartofseries74;81-
    dc.subjectaprendizaje profundoes_CO
    dc.subjectinteligencia artificiales_CO
    dc.subjectrobóticaes_CO
    dc.subjectaplicaciónes_CO
    dc.subjectchatbotes_CO
    dc.titleAprendizaje profundo para selección de opciones numéricas por voz como herramientas para chatbotes_CO
    dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1es_CO
    dc.description.editionVol. 1 Núm. 45 (2025): Enero – Junioes_CO
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    dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2es_CO
    dc.type.coarversionhttp://purl.org/coar/resource_type/c_2df8fbb1es_CO
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