# Robust Deep Speaker Recognition: Learning Latent Representation with Joint Angular Margin Loss
## 8 Conclusions  
There is some work in the field of speaker recognition using traditional handcrafted features and, more recently, deep-learning. SincNet, a deep-learning-based model, achieved state-of-the-art results in speaker-recognition tasks. It is a CNN-based architecture where the initial layer consists of a band-pass-filter-based convolutional layer. Similar to many models predicting probability distribution, the last SincNet layer uses a softmax function of which the output is optimized using cross-entropy loss. From other biometric systems, particularly facial recognition, it can be observed that loss functions leveraging angular margins have been successful. Inspired by such results, this study employed angular-margin-based loss functions, singly and jointly, on the SincNet architecture in a systematic manner.  
This study proposed three different SincNet-based models: AF-SincNet, Ensemble-SincNet, and ALL-SincNet. The proposed models were evaluated with competitive baselines such as SincNet and AM-SincNet. All configurations were tested with different evaluation protocols, namely, intradataset, interdataset, and interlanguage evaluations. In the intradataset experiments, the AF-SincNet model performed better than with the other settings in terms of FER and CER metrics; in the interdataset and interlanguage settings, ALL-SincNet, which employed the proposed joint optimization of three angular-margin losses, performed the best overall. In fact, ALL-SincNet  
Figure 4: Comparison of overfitting problem between SincNet and proposed AF-SincNet.  
Figure 3: FER (%) comparison over training epochs on TIMIT dataset.  
outperformed other models for both interdataset evaluations performed on TIMIT and LibriSpeech. ALL-SincNet also outperformed other settings when trained on the English TIMIT dataset and evaluated on Bangla speaker-recognition data. Even though ALL-SincNet performed worse than the other configurations in intradataset evaluation, it consistently outperformed the other settings in interdataset and interlanguage evaluations. This suggests that the model was not overfitting the intradataset samples and was more robust than the baselines and other configurations, thus indicating the efficacy of jointly optimizing for the three angular margin-based loss functions for this type of task.  
In future work, the proposed models will be tested in a noisy environment setting. We also plan to evaluate larger datasets such as VoxCeleb [26] and VoxCeleb2 [24], which contain millions of samples from more than 6000 speakers.  
conceptualization, L.C. and N.M.; methodology, L.C. and N.M.; software, L.C. and H.Z.; validation, L.C., H.Z.; formal analysis, L.C. and H.Z.; investigation, L.C. and N.M.; resources, L.C. and H.Z.; data curation, L.C.; writing--original-draft preparation, L.C., H.Z.; writing--review and editing, L.C., H.Z. and N.M.; visualization, L.C.; supervision, N.M.; project administration, N.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.