## 2 Background
### Machine Learning Application for Stuttering Detection  
Hidden Markov models (HMM) are widely used in event sequence classification. Therefore, they are particularly well suited for research on speech recognition and related problems. Among the numerous examples of research in speech disfluency is the work of [PERSON] et al. [16]. The authors attempted to detect two subclasses of speech disorder, which were prolongations and pauses in speech. The work included several experiments based on MFCC features derived from speech, the most successful of which was a model trained on isolated samples. It achieved an average accuracy for both studied events of 80%. In conducting the experiments, the authors had at their disposal a database of 500 short utterances recorded by two people with speech disorders. The researchers emphasized that HMM can be a universal method in classifying speech impairment due to the lack of a need to integrate different languages. [PERSON] et al., in their work [33], sought to distinguish between correct and impaired speech without categorizing stuttering. In their study, they used recorded utterances from 10 students, who were asked to record the same short utterance. In addition, they created 15 samples employing a computer-generated speech disorder. Of the 35 samples created, 20 served as a training set for estimating model parameters, while the rest were used as a test set. An accuracy of 93% was achieved using a model based on 12 MFCC features. It is also worth mentioning the article by [PERSON] et al. [15], who used the HMM algorithm to find similarities between speech signals containing speech disorders and those from speakers without disfluencies in speech. For the data collection, 37 stuttering patients of ages between 12 and 45 were recorded. They were asked to read excerpts from the English story \"The Northwind and the Sun.\" The authors concluded that there was a strong correlation between the recordings studied. They were able to distinguish between individuals of different subclasses of speech disorders based on the number and length of breaks in the signal. The Support Vector Machine (SVM) method can be used for both regression and classification [34]. In 2013, [PERSON] and [PERSON] used a multiclass SVM to classify three categories of stuttering, i.e., prolongation and word and syllable repetitions [17]. Experiments were conducted using three different types of acoustic features. These were LPCs (linear predictive cepstral coefficient) [35], MFCCs (Mel-Frequency Cepstral Coefficients) [36], and LPCs (linear predictive coding). They used data from the UCLASS collection. The authors selected 20 recordings of people between the ages of 11 and 20 for the experiments. The efficiency the authors achieved was 92%, 88%, and 75%, respectively. The SVM algorithm was also used by [PERSON] and colleagues [37]. They classified syllable repetition using MFCC features and dynamic time warping (DTW). They trained and tested the algorithm on a dataset containing the recorded utterances of 15 different adults with speech disorders. In the study, the authors achieved an accuracy of 94.35%. [PERSON] and [PERSON] [38] compared the accuracy of SVM using two different kernels of SVM, linear and radial basis function (RBF). A more favorable result was achieved using the linear kernel; its accuracy was 98%. While using the RBF kernel, an accuracy of 96.4% was obtained. In the experiments, the MFCCs coefficients were used as features. Among the classical machine learning methods, one can also see methods using the k-nearest neighbor (k-NN) method, including a model presented by [PERSON] et al. based on a feature vector containing MFCC coefficients developed for detecting repetitions and prolongations in speech [39]. The model achieved an accuracy of 90.91%. The authors used ten recorded reading passages from the UCLASS collection. The samples were selected to cover a wide age range of speech disorders.