The subject of this paper is the presentation of a practically implemented speech recognition model capable of distinguishing words based on artificial intelligence. The paper provides a detailed explanation of the application of a given voice recognition algorithm, implemented using standard deep and convolutional neural networks, the Python programming language, and machine learning libraries Keras and TensorFlow. This machine learning model recognizes several words using neural networks. The core concept of the presented model is the transformation of sound into images (log-spectrograms), leveraging lessons learned from image recognition to identify spoken words (audio recordings). The described voice control model was developed for the needs of the meat processing industry. Primarily, this model was designed for voice-based data entry from livestock ear tags at the slaughterhouse reception. The goal of implementing this model is to reduce human error in manual data entry and facilitate the overall adoption of the information system. The benefits of such a system are numerous. First and foremost, it would increase the speed and efficiency of every worker in all processes, thereby improving overall production. Additional benefits include enhanced tracking of information flow and a reduced risk of errors. Moreover, this system would indirectly contribute to workplace safety by reducing the number of injuries that often occur due to the removal of protective equipment, which complicates computer operation or the completion of paper documents. The proposed system allows computers to be removed from production facilities where climate and working conditions are challenging.

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