Abstract
Speech command recognition (SCR) is the task of detecting predefined voice commands in an audio stream. SCR is used widely in voice assistants, augmented reality, and robotics. Nowadays, Google Speech Commands (GSC) is a benchmark dataset for training and testing SCR models for English. In this work, we developed an open-source speech commands dataset for the low-resourced Tatar language. The dataset employs 35 commands in the GSC dataset and contains 3,547 utterances. To prove the efficacy of our dataset, we trained and evaluated the Keyword-MLP model on our dataset. The model achieved an accuracy of 98.28% on the test set. We optimized the model by converting it to the ONNX Runtime format and deployed it on an NVIDIA Jetson Orin NX 16GB. The optimized model showed an average inference time of 0.003 s on the device, making it suitable for real-time IoT and robotics applications. We have made the dataset, source code, and pretrained models publicly available at https://github.com/IS2AI/TatarSCR to promote the development of voice-controlled systems for the Tatar language.