## 4 Conclusions  
The results of the experiments indicate that SVM proved to be more effective than k-NN. Among the deep models tested, ResNet18 achieved the highest values on the SEP-28k dataset, and the ResNetBiLstm model achieved the best results on the FluencyBank dataset. It should be noted that the deep models are capable of simultaneously making predictions for each of the classes, unlike the SVM algorithm, which had to be trained separately for each class. The deep learning model achieved significantly higher values for the F1 measure. The most challenging classes to discern were found to be repetitions of both sounds and whole words. One reason may be the small number of positive samples for these classes in the dataset. Interjections and blocking were recognized with similar success. Prolongations are also among the more difficult subcategories to detect. The reason behind this may be the annotations available in the SEP-28K collection. It should be remembered that they were made by individuals who do not work with stuttering people daily. They only received training under the guidance of clinicians. However, their annotations are independent of each other, and the final grade assigned to a given fragment resulted from averaging these labels. A real benefit of making the SEP-28K collection available is the amount of data it contains. The experiments show that the number of samples in the test set significantly impacts the final result. Therefore, this is one of the constraints that was uncovered in the study. Moreover, some other limitations of such a study can be discerned. The most important seems to lie in the nature of the stuttering phenomenon. Some classes of disfluencies are less commonly seen than others, so the datasets contain imbalanced data. Moreover, if such stuttering-like events are searched automatically, then pauses, repetitions, hesitation in talking, etc., will also be treated as stuttering occurrences, even if they are not. One solution seems to be a multimodal approach in which disfluency events in speech are checked against blocks appearing on the speaker's face.  
The study found that the way the data are divided has a significant impact during training. An experiment on the effect of particular features showed that they carry additional information about the data, which translates favorably into the final result of speech disorder detection. Therefore, this is another constraint limiting the study. There are a  
Figure 17: Performance of deep learning models on the FluencyBank dataset.  
plethora of features and feature/algorithm combinations that will influence the outcome of the study.  
It was also found that it was not necessary to separate the model into two branches, one to make a binary prediction of the speech disorder and the other to classify specific subcategories of stuttering. A better result was achieved on the test set by models with a single path for predicting particular classes, with the prediction for the general class determined based on that. This led to a reduced model, which translates into a shorter inference time. Moreover, less training time and computational resources are required. All the experiments were performed on a laptop with a single NVIDIA RTX3070-class graphics card with 8 GB of built-in RAM. It should, however, also be mentioned that each model could be trained for a much larger number of epochs. Moreover, the ResNet18 model could be extended to include more convolutional layers, depending on the resources available.