## 2 Background
### Datasets Available
It can be observed that the ratio of samples with any speech disorder to samples with fluent speech is about 44:56, so this is a balanced dataset, considering the general class. The percentage of the occurrence of other disfluency types, unfortunately, is not as good. Most of them amount to about 10% of the entire set. In contrast, interjections amount to 21.2% of the collection. Such a disproportion of data in particular classes may result in difficulties in training a machine learning model capable of correctly classifying all disorder events. Classes unrelated to speech disorders make up a small percentage of the data. The only marking that frequently occurs, in as much as 8% of the samples, is natural pausing, which is insignificant in sample quality. SEP28-k was more extensively described as it was used in the present investigation; however, there are other databases available, including UCLASS [10], LibriSpeech [29], TORGO [30], and FluencyBank [31]. The FluencyBank database contains many recordings of children and adults with various types of speech disorders, including stuttering [31]. The labels for these recordings are transcriptions saved in CHAT format. Unfortunately, not all have text transcripts and are labeled with speech disorders. The disadvantage of this collection is that the pathologies in speech, unlike SEP-28k, are labeled phonetically. To use the data from FluencyBank, the CLAN program must be employed. There are also inconsistencies and inaccuracies in the labeling provided by the original data authors [14]. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Classes Related to Stuttering**} & \multicolumn{1}{c}{**Frequency of Occurrence [\%]**} \\ \hline Breaks & 12 \\ Prolongations & 10 \\ Repetitions of sounds & 8.3 \\ Repetitions of words or whole phrases & 9.8 \\ Any disruption of speech & 44.1 \\ Interjection & 21.2 \\ \hline \multicolumn{1}{c}{**Classes not related to speech impairment**} & \multicolumn{1}{c}{**Frequency of occurrence [\%]**} \\ \hline Natural pause in speech & 8.5 \\ Unintelligible speech & 3.7 \\ Uncertainty of the evaluator & 0.1 \\ Lack of speech & 1.1 \\ Poor audio quality & 2.1 \\ Audible music & 1.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of classes in the SEP28-k dataset [14]. Repeating the labeling process of the FluencyBank data, analogous to the labeling process in SEP28-k, resulted in 4144 short clips (3.5 h) annotated according to the following categories [14; 31]:  
* Classes related to speech disorders: breaks (10.3%), prolongations (8.1%), repetitions of sounds (13.3%), repetitions of words or whole phrases (2.5%), any speech disorder (46.9%), and interjections (27.3%);
* Classes not related to speech disorders: natural pauses in speech (2.7%), unintelligible speech (3.0%), and uncertainty of the evaluator (0.4%). The UCLASS database contains recordings of people who stutter [10]. The authors have made both phonetic and orthographic transcriptions available. Files containing information about the recordings, such as the environment and the person's condition, are also available. Markers are provided in CHAT SFS and PRAAT TextGrids formats. Three versions of the data are available. The first is from 2004--in which only one category is available, a monologue. A total of 138 people were recorded, 18 of whom were women. The second edition is from 2008--three types of recordings are available: reading, monologue, and conversation. There are 128 conversational recordings of various people available, of which 18 are women. For reading, there are 107 people (15 women, 93 men), while for monologue, there are 82 people (6 women, 76 men). Finally, the third release refers to modifications to version one by changing the recording frequency. The LibriSpeech dataset represents a corpus of English for automatic speech recognition (ASR) research [29]. The LibriSutter collection is a modified subset of the data included in LibriSpeech. It contains recordings of 23 men and 27 women.