## 5 Discussion
Models using MFCCs, RASTA features, or their combination were analyzed, showing no significant performance difference between all features, CFS-selected subsets, and the top 50 features. The top 50 feature set was preferred for its simplicity and reduced overfitting risk [39]. When tested on clean data, the three feature families offered comparable performances, with the RASTA family being the worst. This aligns with the ongoing debate in the literature regarding the optimal features for SR. However, the introduction of noise and reverberation profoundly altered the system's behavior. Specifically, MFCC features exhibited a marked decline in class 1 recall, indicating reduced capability to identify the primary speaker against the background corpus in verification tasks. Conversely, RASTA-PLP features maintained consistent performance in verifying the primary speaker but showed diminished recall for class 2 (\"all\"). This behavior, although different for each speaker, is consistent in general throughout the whole dataset regardless of the classifier. Additionally, reverb posed a greater challenge than noise, with performances generally being worse. This is in line with the fact that, instead of being pseudo-random and additive like noise, reverberation is a complex phenomenon that is modeled as the summation of filtered and delayed copies of the original signal, in fact creating a superposition of speech signals. The characteristics of the reverb depend solely on the acoustic scenery, which means that different speakers are affected by similarly filtered artifacts. Both MFCCs and RASTA features struggle with reverberation, which proves that it is a crucial phenomenon to be aware of in real-world speaker recognition problems. Speaker verification is a common biometric solution that finds a great number of applications in modern technology, the most obvious one being security when accessing devices, accounts, or locations, as well as fraud prevention and customer authentication. Smart home devices and banking services use it for personalized interactions and secure transactions. Law enforcement uses it for criminal identification and surveillance, while education relies on it for exam integrity and attendance. With these premises, sensitivity is generally more important in user-centric systems where ease of access is prioritized and the identification of the main speaker is crucial. On the other hand, specificity becomes equally important in high-security or fraud-prone environments where the cost of allowing imposters is high. MFCCs, despite offering consistently better performances than RASTA features alone, struggle greatly to identify the main speaker (low sensitivity). This leads to speculation that user-centric speaker verification systems that are usually also more prone to non-controlled, noisier environments, should favor models trained with some kind of noise/reverb robustness, such as the introduction of alternative features alongside MFCCs. The present findings confirm that MFCC and RASTA-PLP features are differentially sensitive to noise and reverberation due to their distinct frequency domain filtering techniques. The RASTA method of removing slowly varying components and constructing an all-pole model of the re-transformed output differs significantly from the Discrete Cosine Transform (DCT) approach used in MFCCs. Combining MFCC and RASTA features leverages the strengths of both, resulting in a hybrid feature set that maintains stable recall rates for both primary and background classes, with only a minor decline in the latter. This result supports the hypothesis that neither MFCCs nor alternative features alone can comprehensively capture the full spectrum of discriminative characteristics needed for effective speaker verification. RASTA-PLP features, despite their potential to mitigate phenomena like slowly varying noise, face challenges in interpreting which speaker attribute to isolate.