Abstract
Speech emotion recognition task, as well as most audio recognition machine learning tasks, uses the so-called framing. This is the process of dividing the original audio signal into frames of a certain size, each of which is processed separately. This article presents a comparison of the effect of frame size on the emotion recognition result using a CNN network as an example. For the experiments, the CREMA-D dataset was used with the augmentations using noise adding, time stretching, and pitch shifting. We managed to achieve a recognition accuracy of 98.8% using dynamic frame size.
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