# An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments
## 4 Results and Discussion
### Experimental Results
This further highlights the effectiveness of the MLP model, particularly when using MFCCs in combination with VAD, which leads to superior validation performance. In both Figures 11 and 12, it can be observed that MFCCs with VAD outperform both MFCCs without VAD and spectrogram features with and without VAD. These results also suggest that spectrograms perform poorly for speaker identification compared to MFCC features. After identifying the best feature set with VAD, we compare our proposed model, i.e., the MLP with softmax, against LSTM and BiLSTM classifiers. The accuracy results of the MLP, LSTM, and BiLSTM classifiers are shown in Table 5.  
Figure 11: MLP model accuracy. Figure 10: MLP model validation loss. Note that all of the MLP, LSTM, and BiLSTM models are compared on the following settings:  
* VAD performed;
* MFCC features extracted;
* Softmax output activation function. It can be seen that the proposed system outperforms the LSTM and BiLSTM classifiers. Figure 13 compares the model accuracy and validation accuracy of the MLP, LSTM, and BiLSTM models. The error rate is consistently lower in the MLP compared to the LSTM and BiLSTM models, which demonstrates more accurate speaker identification. The sharp decline in the MSE for the MLP confirms its ability to reduce prediction errors in less time than more established methods, such as those using VAD. The MSE in the LSTM and BiLSTM models remains slightly higher throughout, suggesting that their sequential nature may introduce more variability in predictions, especially when noise affects the data. Figure 14 compares the model loss and validation loss of these models, showing that the validation MSE for the MLP continues to be lower than that of LSTM and BiLSTM, proving that the MLP has a better ability to generalize successfully on unseen data. The relatively stable and lower MSE curve for the MLP supports the hypothesis that MFCC features combined with VAD enhance noise robustness. The MSE for both LSTM and BiLSTM, though slightly lower than for MLP, highlights their sensitivity to noise and variable-length data. To evaluate the performance of our proposed system, we compare it with the following baselines. Baseline 1: We use a multilayered perceptron without a softmax output layer and with VAD as our first baseline. Baseline 2: We use a multilayered perceptron with a softmax output layer and without VAD as our second baseline. We compare the proposed system against these two baseline models, as well as the two following recurrent neural network (RNN) architectures: Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). Figure 12: MLP model validation accuracy. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **MLP** & **LSTM** & **BiLSTM** \\ \hline Test Accuracy & 0.994 & 0.762 & 0.929 \\ Test Loss & 0.088 & 0.603 & 0.263 \\ Test Mean Squared & 0.005 & 0.0372 & 0.0218 \\ Error & \multicolumn{1}{c}{} & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics for different models. The results clearly show that our method outperforms the two baseline models. The first baseline, based on an MLP with VAD but without the softmax output layer (Baseline 1), provides lower accuracy. This indicates that including softmax allows the model to extract speaker information more effectively and distinguish it from noise, which is crucial in noisy environments. Baseline 2, which lacks VAD, performs the worst of all systems (and significantly worse compared to the proposed system), underscoring the importance of VAD in reducing noise and improving overall accuracy. The proposed system also outperforms both LSTM and BiLSTM, as shown in Figures 15 and 16. This result is somewhat surprising given that RNN architectures like LSTM and BiLSTM are expected to perform well on sequential data. However, the integration of MFCCs with VAD and softmax appears to provide a more powerful feature set for the MLP, outperforming these sequentially concatenated models.