## 2 Background
### Machine Learning Application for Stuttering Detection
Among the data chosen, manual segmentation of the impairment was performed, extracting 55 prolongations and 55 repetitions. The same authors also investigated the effectiveness of LPCC features for the same speech disorders, as in the previously mentioned work [40]. The data used in this experiment also overlap with their previous work. An accuracy of 87.5% was achieved for the linear discriminant analysis (LDA) model and 89.77% for k-NN [40]. A noteworthy paper using these models is a 2017 paper by [PERSON] et al. [41]. The presented model was capable of distinguishing between several speech disorders simultaneously. The model was trained on selected data from the UCLASS collection. Thirty-nine recordings with 18 participants were used. After averaging, the accuracy was 52.9% for disorders such as repetitions, prolongations, and fluent speech. Neural networks were used in speech disorder detection even before today's level of technology and computing power were available. As early as 1997, [PERSON] and his team designed a system for stuttering detection consisting of two stages [42]. The first was a model whose task was to segment speech into linguistic units, and then, in the second stage, another model assigned the created segments to the appropriate categories. Recordings made while reading the fairy tale \"Arthur the Rat\" were used as a dataset. Twelve children with speech disorders participated in the study. The story contains 376 words, of which 90% are monosyllabic. An accuracy of 78.01% was achieved on average in classifying the words spoken incorrectly. [PERSON] et al. used neural networks to detect stuttering among children [43]. The dataset contained 51 study participants. The model was trained based on information such as the type of disorder, its duration, gender, age, family information, and the child's behavioral assessment. In the study, the authors declared an accuracy of 92%. The detection of individual categories of stuttering was also carried out by [PERSON] and colleagues in 2014 [18]. The result obtained was an accuracy of 76.67% for pauses in speech. They used 40 fragments of 4 s containing disordered speech and the same number of recordings of correct speech. Kohonen self-organizing maps (SOM) and a multilayer perceptron were used for classification. In 2013, [PERSON] and [PERSON] presented a new type of MFCC based on the linear prediction-Hilbert transform-based MFCC (LH-MFCC) [44]. The authors declare that the improved features contain more information related to voice, articulation, and pitch. They reported a 1.79% improvement in performance over classic MFCC coefficients, measuring the average accuracy of the model on a test dataset. The significant increase in available computing power over the past few years has led to a considerable increase in interest in deep learning, including in the field related to signal processing. A number of methods were developed that can effectively handle many types of tasks, including the classification of speech disorders. In 2021, research work was published on the development of a deep neural network for classifying subtypes of speech disorders called StutterNet [25]. The authors used the first release of the UCLASS dataset for the study, from which they selected recordings of 128 persons. StutterNet contains five layers of time delay that precede pooling layers, three layers of fully connected artificial neurons, and a softmax layer. The last layer returns predictions for all diagnosed classes. In addition, the ReLU activation function and data normalization were used after each of the mentioned elements. Moreover, after the first two fully connected layers, a dropout with a likelihood ratio of 0.2 was included. To prepare the data, the authors determined 20 MFCCs from each sample. A \(k\)-fold cross-validation method was used, with a \(k\) equal to 10. Ten experiments were conducted, in each of which 80% of the samples were randomly assigned to the training set, 10% to the validation set, and 10% to the test set.