## 5 Discussion  
In speaker recognition, noise and reverberation pose significant challenges because they distort the speech signal, reducing the accuracy of recognition systems. Moreover, they are mostly present in real-world test data, which makes it increasingly difficult to assess the performance of the chosen model aprioristically. This study does not aim to design a state-of-the-art SR pipeline; rather, it studies the characteristics of the input features that can be used to minimize the effects of noise and reverberation, which are unavoidable. Other augmentation techniques, such as noise removal, can be employed in addition, but the choice of the optimal starting feature set is still crucial, especially considering the fact that many SR tasks still start from MFCCs or similar features to this day. The techniques that are currently being employed to mitigate the effects of noise and reverb include the following:  
* It is necessary to further process the features, starting in particular from MFCCs or similar, in order to derive noise-robust alternatives. These include computations such as cepstral mean and variance normalization (CMVN), PLP, gammatone, or RASTA. Although the aim is to \"remove\" the effects of noise from MFCCs, the result is a different set of features. * Noise removal, usually employing either spectral subtraction, Wiener filtering, or Minimum Mean Square Error (MMSE), is necessary. This involves directly processing the original audio source to attempt noise removal. Although it creates artifacts on the signals, many studies report an increase in performance. * Voice Activity Detection (VAD), which removes portions of the signal that are deemed as noisy while leaving the other ones untouched, is required. * De-reverberation, usually performed by modeling the reverb response through linear prediction and inverse filtering, can be utilized. * It is necessary to perform data augmentation on the training set, adding noise, reverb or other artifacts, which inherently makes the model more robust to such phenomena. The usage of \"black-box\"-like architectures, especially DNNs, is also employed, as these models should map noisy/reverberant high-level speech features. Although they offer good performances, they are hard to interpret and may lack in generalization and are out of the scope of the present study, which aims to stress the power of specific input features. The current state-of-the-art technique of speaker verification basically employs i-vectors trained with MFCCs or their variants, or deep speaker embeddings, to map the features of a voice into a vector representation of constant dimensions in a multidimensional space, usually employing x-vectors and deep networks for this. Although the present study reaches accuracies up to 99% on a 56-speaker dataset for certain subjects, perhaps demonstrating how a good choice of features may provide high performances, even with a \"basic\" model such as an SVM, classification performance per se was not the main aim of the paper. The problem of removing the effects of noise and reverberation in SR can be tackled from many points of view, and is ultimately a domain adaptation problem. Starting from the correct features, techniques such as VAD, noise removal, further augmentation, and de-reverberation can be used in conjunction to further improve the robustness of the model. This study only concentrates on the first, basic step of domain adaptation, which is the choice of robust features, proving that MFCCs perform much better when accompanied by a noise-robust, processed counterpart such as RASTA features. This study evaluated the impact of noise and reverberation on speaker recognition (SR) models, proposing a multi-domain feature subset combining MFCC and RASTA-PLP features. Noise and reverberation significantly degraded the model's accuracy, with extreme cases dropping below 2%.