In this work, the focus was on methods of combining classifiers. Base algorithms were not described in detail but used as a foundation with the assumption of knowledge in the field of machine learning. Initially, several combiners, i.e., algorithms that create a synthesis among the outputs of multiple estimators, were explored. Then, the most commonly used algorithms in ensembles were presented and driven by the desire to create an optimal ensemble, key elements of a successful ensemble were described through the concept of diversity. Subsequently, some of the presented methods were tested on the problem of speech recognition using the well-known benchmark dataset Librispeech. The problem is addressed using advanced Transformer Neural Networks, demonstrating high precision of the model in speech recognition. An extensive analysis was conducted, employing most of the presented algorithms along with five visual representations of audio signal. Results were graphically displayed and explained using measures of diversity. Ultimately, this work confirms the hypothesis that ensembles, when used correctly, surpasses standard models, whether it be Linear Regression, KNN, or Transformer Neural Network.