## 3 Method
### Impact of Feature Types  
Spectrograms and mel spectrograms, i.e., 2D feature spaces, are widely used in speech classification [50], so it is worth employing them as one of the representations incorporated into the deep models. Moreover, even though chroma features are primarily dedicated to music analysis [51], such a representation in the form of 2D feature space, i.e., chromagrams, is employed in speech analysis as well [50], but with less success [52]. One of the properties of chroma features is that they capture harmonic and melodic characteristics of music while being robust to changes in timbre and instrumentation [53]. In stuttering detection, this can be important because of the lack of impact caused by different voice timbre in speakers, so chromagrams are to be used as well.  
Therefore, the performance of the models was examined using selected features as input and their combination. Spectrograms were created using the libros Python language library. The window size was set to about 25 ms, taking into account the sampling rate of 16 kHz. The parameters of the learning rate were chosen based on the results of the previous study. To perform this experiment, the size of the input data of each model had to be changed slightly. For the baseline model, which has the LSTM cell first, only the number of features in the layer input was changed, but the number of consecutive time steps remained the same. In the case of architectures that have weave layers in the first place, it was not necessary to change the size of the filters. Only one of the input dimensions changed, which only affected the output height. The chromagram was considered a complementary feature, but it was employed to see how deep learning models would perform with only this information. Finally, mel spectrograms were also tested combined with the models proposed. Variations in the configurations of model architectures were analogous to the experiment with spectrograms. An identical strategy was adopted for the selection of the learning rate. The results are shown in Figures 7-11.  
The use of only chromagrams yielded the weakest results, probably because these features only carry information about the pitch in a given time window. While this is insufficient data to classify speech disorders effectively, it can be a valuable addition to the input information.  
In addition, the performance of the models was checked using mel-scale-based spectrograms as input. Such a representation is often used in speech recognition [50; 52], so it was deemed worth testing (see Figure 11).  
After analyzing the results, we can see that the ResNetBiLstm and ResNet18 models can more accurately recognize speech disfluencies, confirming previous conclusions. The models seem to classify better when trained on a combination of all features, which are MFCCs, spectrograms, and chroma features. The exception here is the ResNet18 model, which is already a complex enough model that it may have been subject to some degree of overtraining.  
Figure 7: Values of the F1 measure for the baseline model on test set after training on specific features.  
Figure 8: Values of the F1 measure for the ConvLSTM model on the test set after training on specific features.  
Figure 10: Values of the F1 measure for the ResNet18 model after training on specific features.  
Figure 9: Values of the F1 measure for the ResNetBiLstm model on the test set after training on specific features.