# Automatic Age and Gender Recognition Using Ensemble Learning
## 1 Introduction  
Speech production is a complex process that begins in the brain and requires the coordination of the lungs, larynx, vocal cords, tongue, lips, mouth, and facial muscles. It depends on many anatomical features such as the dimensions of the mouth, pharynx, and nasal cavity; the shape and size of the tongue and lips; the position of the teeth; and the elasticity and density of the tissues [1]. These features vary from person to person, and accordingly, the speech produced by each person is different. In this context, a speech signal contains verbal content as well as information about the speaker, such as his/her identity, language, age, gender, accent, and emotional state [2]. Human beings can easily extract this information from speech signals and use it frequently in their communication with each other. The information outside the verbal content of the speech is called paralinguistic information, and this information is extremely important for human-computer interactions. Recently, paralinguistic information extracted from speech has begun to be used in many areas such as banking systems, call centers, intelligent voice assistants, security, and advertising [3, 4]. Age and gender are among the most important pieces of paralinguistic information, and this information can be used for different purposes such as determining the appropriate customer representative; selecting music, advertising, or different content; or defining authorizations to access certain resources [5, 6, 7]. In addition, age and gender recognition can be used as preprocessing in other speech-based systems such as speaker, speech, or emotion recognition to develop age- and gender-specific models and their performance can be increased by using these models [6]. There are many studies on recognizing the age and gender classes of the speaker from the speech signal. In some of these studies, the age and gender information of the speakersis considered together, while in others, this information is evaluated separately [7; 8; 9; 10; 11; 12; 13]. In gender recognition, it is aimed to determine the speakers as male and female and in age recognition, it is aimed to determine the speaker's exact age in years or the age group such as young, adult, or elderly. In the studies where age and gender are examined together, speakers are classified according to definitions that indicate both their age and gender groups, such as young females. As for age groups, class definitions such as young, adult, and elderly or with age ranges in the tens such as the twenties, thirties, and forties are generally used. On the other hand, some studies specifically focus on child speakers [14; 15]. In this research, age and gender classifications were considered together and a classification with a total of twelve classes was carried out, defined by the combination of two classes (male and female) according to gender and seven classes (20s, 30s, 40s, 50s, 60s, 70s, and 80s) according to age. This grouping is more inclusive than the grouping of young, adult, and elderly and is therefore a more challenging task. The task of recognizing the age and gender of the speaker from the speech signal consists of two critical stages, namely feature extraction and classification. The first of these steps, feature extraction, is the process of extracting measurement values representing the speech and the speaker from the speech signal, and the effectiveness of this process directly affects the recognition rate. There are various methods used in the literature to extract features from speech signals. Mel spectrogram, mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), spectral features, chroma, fundamental frequency (F0), zero-crossing rate (ZCR), and harmonic features are some of the most commonly used speech features [16; 17]. After the feature extraction stage, a machine learning model is trained with the determined feature vectors, and the model is evaluated with the test dataset.