## 6 Conclusions  
This paper was focused on the evaluation of the effect of real-world noise and reverberation on already trained speaker recognition models, and on the identification of an optimal feature set that could encompass more than one standard feature family, exploiting the advantages of different processing solutions. One-vs-all classifiers were trained on the DEMoS speech dataset, balanced in terms of emotional content, to perform speaker verification tasks aimed at identifying the \"main\" speaker within the corpus. The test set was augmented with noise at varying signal-to-noise ratios (SNRs) and with convolutional reverb at different signal-to-reverb ratios. Features from the MFCC and RASTA-PLP families, along with their delta and delta-delta functions, were extracted, and an algorithmic non-linear feature selection process was applied to determine the optimal subsets for MFCCs, RASTA features, and their combination. The results, averaged across 50 speakers, showed accuracies ranging from 99% to 56% on raw audio data. While the proposed model demonstrated high effectiveness on non-noisy test data, its performance declined progressively as the level of noise or reverberation in the test data increased. Interestingly, MFCCs decreased their performance in the recall of class 1 (main speaker), whereas RASTA features showed the opposite behavior. On the other hand, the optimal feature subset that contained both MFCC- and RASTA-related features showed consistent performances even on very noisy or reverberated data, with no sensible decay aside from a slight worsening of class 2 recall (rest of the corpus) and accuracies consistently over 80%. The conclusion of the present analysis can be summarized as follows:  
* On non-noisy datasets, MFCCs and a merged (MFCC + RASTA) feature set offer comparable performances, with the latter being negligibly better;
* When speaker verification models are used on noisy or reverberated validation data, however, performances decay with the increase in noise or reverb;
* Performances with reverberated data are generally worse than those with noisy data, which is in line with the non-additive, more complex nature of the reverberation phenomenon;
* MFCCs lose sensitivity and are worse at identifying the main speaker vs. the rest;
* RASTA features lose specificity and are worse at verifying that the speaker is not the main;
* The merged feature set offers the best compromise between sensitivity and specificity;
* The merged feature set, picked by a correlation-based selector, comprises 57% MFCC- and 43% RASTA-related features as further proof of their complementarity. The use of MFCCs has always been a gold standard and still is for many speaker recognition solutions. Considering that many speaker verification tasks, especially the user-centric ones, favor sensitivity over specificity, and that the ones related to security should strive for the compromise, it is reasonable to conclude that MFCCs alone, although exhibiting good performances when trained and tested on raw datasets, may not be sufficient. The addition of an alternative, inherently noise-robust feature family such as RASTA offers a good compromise on the studied dataset. Future research will expand the findings of the present paper to other possibly bigger datasets and will include less interpretable classifiers in general, such as x-vectors and deep embeddings. Moreover, the role of solutions such as training data augmentation and validation data polishing using noise/reverb removers will be explored with a more comprehensive assessment of the plethora of techniques, pairing the choice of the optimal feature set to other techniques such as denoising, de-reverb, or VAD.