Resumen

PATINO, Mariano Martínez et al. Application of CNN in Audio Recognition of Isthmus Zapotec. RISTI [online]. 2025, n.59, pp.36-52.  Epub 30-Sep-2025. ISSN 1646-9895.  https://doi.org/10.17013/risti.59.36-52.

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 ​​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.

Palabras clave : Intercultural communication; zapotec indigenous languages; spectral images; deep neural network.

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