<![CDATA[Technology in the field of speech recognition is currently experiencing rapid progress. One technology that utilizes speech recognition is virtual assistants such as Google Assistant, Cortana, and Alexa. In order to improve the quality of communication between virtual assistants and humans, virtual assistants need to know who their communication opponents are. One way is by knowing the gender and age. Recognition of gender and age range based on voice is one part of speech recognition. Audio cannot be directly classified, therefore there is a need for feature or feature extraction, feature extraction that can be used include Mel-Spectogram, Mel Frequency Cepstral Coefficients (MFCC), and Chroma- Short-Time Fourier Transform. Artificial neural network architecture is able to classify it, one of the methods is Backpropagation. From the tests conducted by gender classification and age range with a dataset from Mozilla Common Voice, the accuracy is less good, that is 0.18357. From the test results it is necessary to do additional testing, namely testing the dataset. When testing the dataset for gender classification alone, the accuracy of classification with the Mozilla Common Voice dataset is 0.62504, while the accuracy of the classification with the dataset from Free ST American English Corpus gets 0.9349. From the tests conducted it was concluded that the use of the Mozilla Common Voice dataset was less recommended for gender and / or age recognition.]]>
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