## 1 Introduction  
In parallel with technological advancements, the interaction between humans and computers has increased, and the use of technologies that employ more complex methods, such as speech recognition in human-computer interaction, has become widespread [1]. In systems utilizing speech recognition technologies, artificial intelligence processes the speech signals to determine the necessary actions based on the obtained information. Speech production is a complex process performed by humans. The resulting speech signal contains linguistic information such as vocabulary and syntax, as well as paralinguistic information like the speaker's age, gender, accent, and emotional state [2]. The paralinguistic cues that people often use to communicate with each other have now started to be utilized in communication with computers. The gender of the speaker is one of the most salient paralinguistic cues, and this information is widely utilized in personalizing services. However, recognizing the gender of the speaker from speech signals is a challenging task. The gender recognition task is utilized in many applications for various purposes, such as developing effective advertising and marketing strategies, enhancing human-machine interaction, identifying criminals, and increasing customer satisfaction through personalized user services [3]. Moreover, gender information is also used in other speech-based recognition systems for purposes such as limiting the search area to speakers of the same gender or developing gender-specific models, thereby increasing the accuracy and speed of these systems [4]. Various factors influence the accuracy of automatic gender recognition systems, including voice quality, diversity, feature selection and extraction, and the design and evaluation of classifier models [5]. Among these factors, choosing and extracting suitable featuresfrom the speech signal is one of the most crucial steps that directly impacts the recognition rate [6]. Mel scale power spectrogram (Mel Spectrogram), Mel frequency cepstral coefficients (MFCCs), spectral contrast (Contrast), power spectrogram chroma (Chroma), and tonal centroid features (Tonnetz) are some of the most commonly used speech features. However, none of these features represent all aspects of speech, so the feature extraction process needs to be tailored for each task. After extracting features from the speech signals, they are fed into a classifier along with class labels indicating the speaker's gender, and then the classifier is trained. Afterward, a dataset containing new speech data that were not used in the training phase is used to test the performance of the model on unseen data. Many methods have been developed for classifying input data represented in different feature spaces. Each method's problem-solving approach is different; accordingly, each method has different advantages and disadvantages [7]. Previously, traditional machine learning methods such as Hidden Markov Models (HMM), Gaussian Mixture Models (GMM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM) were commonly used for gender recognition [8; 9]. In recent years, deep learning has become one of the most popular areas of machine learning due to its success in various fields such as computer vision [10], natural language processing [11], and speech recognition [12]. Deep learning algorithms tend to perform better when there are a large amount of training data available. However, they require powerful GPUs and significant memory space for their training. Moreover, they are highly reliant on data quality, and their performance is adversely impacted by noisy, incomplete, or biased data [13]. On the other hand, traditional machine learning algorithms are more suitable for small datasets and do not require significant computing power [14]. In this study, we focus on feature extraction methods. We compare the gender recognition performances of various features such as MFCC, Chroma, Mel Spectrogram, Contrast, and Tonnetz.