## 1 Introduction  
This work deals with automatic stuttering detection through machine learning. An algorithm capable of effectively classifying stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. Despite its many potential applications, automatic stuttering detection employing deep learning appears sporadically in the topic of speech processing and machine learning [1; 2; 3; 4]. At the same time, it should be noted that deep learning has dominated many areas related to speech recognition, such as speech recognition and speech synthesis [5; 6; 7; 8; 9]. The problem of speech impairment refers to difficulties in maintaining fluency when speaking. One category of speech disorder is stuttering, which is very complex due to its multiple subtypes in etiology [10]. It involves the involuntary addition of sounds and words and/or the inability to maintain speech fluency. It also refers to repetitions,prolongations (difficulties with specific sounds or syllables as in part-word repetitions and prolongation), blocks while speaking words, word repetitions (or pauses in speech), and interjections (adding extra sounds, syllables, or words). It affects about 70 million people worldwide, roughly 1% of the population [11]. However, stuttering does not always affect people who have a congenital speech disorder. Even healthy people in situations of stress or nervousness can sometimes lose fluency and freedom of speech. Such a situation, however, does not imply a pathology. In addition, people who stutter suffer from different degrees of severity of the condition depending on the situation. It may arise during a conversation or while performing daily activities; it both may or may not be associated with social anxiety, which worsens this condition. People who stutter face difficulties in social and professional interactions, as well as an adverse public attitude toward stuttering [12]. Communicating with new technology through virtual voice assistants such as digital banking, flight customer services, and chatbots, and even Alexa, Google Assistant, and Siri can also be problematic [13]. Speech fluency disorders are not easily defined, as they are influenced by many factors, such as gender, age, accent, and the language spoken by the speaker. There are many classes of stuttering, each with its subclasses, which makes identifying all types of stuttering with a single model a difficult task. Even a specific kind of stuttering applied to a single word can be analyzed in different ways. A common problem in training deep learning algorithms for speech disorder detection is the lack of sufficient training data. Many works rely on their own hand-recorded, transcribed, and labeled datasets, which are often relatively small due to the limited work put into creating them. The most widely used in the speech disorder field is the UCLASS (University College London Archive of Stuttered Speech) dataset [10]. A dataset released in 2021 by Apple, SEP28-k [14], contains approx. 28,000 short recordings containing speech disorders and may prove to be a breakthrough in this regard. Methods for detecting stuttering use spectral features such as Mel-Frequency Cepstral Coefficients (MFCCs), Linear Prediction Cepstral Coefficients (LPCCs), pitch, zero-crossing coefficient, etc. Moreover, spectral analysis resulting in a 2D (two-dimensional) feature space is also employed. These features in the form of parameter vectors are analyzed by using statistical modeling methods such as hidden Markov models (HMM) [15; 16], support vector machines (SVM) [17], neural networks [18; 19], and rough sets [19]. Moreover, methods such as wavelet analysis [20] and dynamic time warping (DTW) [21] are also employed in stuttering analyses [22; 23; 24]. More recently, a 2D feature space was combined with deep learning algorithms [1; 2; 3].