## 2 Background  
### Datasets Available  
In this section, datasets containing speech disfluencies are recalled, as they constitute an essential part of the machine learning approach to the given problem. They may also be helpful for both researchers and therapists as examples of how to archive data when collecting samples of stuttered speech. SEP-28K [14] is the most extensive collection of labeled stuttering samples published, containing multiple classes for both speech impairment and fluency in speech. It includes recordings from 385 interviews created through 8 series conducted with people with speech disorders. These episodes involve selected radio plays: He stutters; HVSA; I stutter, so what; My stuttering life; Stutter talk; Stuttering is cool; Women who stutter; Strong voices [14]. Between 40 and 250 short, 3 s segments were cut from each episode. Their final total number was 28,177. The editing of the recordings was conducted automatically using a speech detector. According to the authors, speech disorders most often occur just after, before, or during pauses in speech, and for this reason, the aforementioned several-second blocks are located around such pauses. In addition, to obtain the greatest variety in the labeled disorders, the recordings were cropped differently with respect to the localized breaks in speech. Each clip was annotated independently by three people. The evaluators received brief training in recognizing stuttering and its subcategories, but none professionally dealt with the disorders in question. This caused some errors in annotating due to the difficulty arising from labeling with audio only. Unlike other collections, the authors did not have access to any additional auxiliary information, such as physical or visual signs of stuttering. A list of classes annotated as part of the collection is presented below. It is worth mentioning that each block can be assigned to multiple classes, both related to speech disorders and those not caused by disfluency [14]:  
1. Block--a pause in a speech indicating taking a breath or stuttering;
2. Prolongation (prolongation)--prolongation of a single vowel, e.g., \"M[mmm]ommy\";
3. Syllable repetition (sound repetition)--repetition of a single slab or sound, e.g., \"I [pr-pr-pr-]prepared dinner\";
4. Word or phrase repetition (word/phrase repetition)--for example, \"I made [made] dinner\";
5. Interjection--the insertion of a word or voice into an utterance, e.g., \"um\", \"uh\";
6. No dysfluencies. The recordings also have non-speech disfluency labels to assist in speech processing:  
1. Pause (natural pause)--a natural pause in speech not caused by stuttering;
2. Unintelligible passage (unintelligible)--the authors were unable to understand the speaker's message;
3. Unsum--the denominator was uncertain into which class or classes the block should be classified;
4. No speech (no speech)--there is silence, or only noise occurring in the background;Poor audio quality (poor audio quality);
* Music--no speech, while background music can be heard. As mentioned earlier, to minimize errors, each recording was labeled by three independent evaluators. The inter-rater reliability between the evaluators assigning grades was measured using [PERSON] kappa measure [28], which is calculated according to Equation (1) [14]. \[\kappa=\frac{P_{o}-P_{e}}{1-P_{e}} \tag{1}\]  
where:  
- \(P_{o}\)--observed agreement between evaluators;  
- \(P_{e}\)--expected agreement when data are randomly labeled. [PERSON] kappa measure ranges from 0 to 1, with 1 indicating complete agreement and 0 indicating no compliance. The highest agreement was observed for the syllable repetition, interjection, sound repetition, and no disturbance classes, amounting to 0.62, 0.57, 0.4, and 0.39, respectively. The lowest score was obtained for interruptions and prolongations, at 0.25 and 0.11, respectively. This may be explained by the lack of additional modality beyond the audio signal when evaluating a speech sample. Table 1 shows the distribution of classes in the SEP28-k dataset.