# An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments
## 4 Results and Discussion
### Experimental Results  
After extraction of the four types of features, these are embedded in the multilayer perceptron with a softmax activation function. Table 2 shows the selected hyperparameter settings. The results of the experiments are shown in Table 3, which shows that the proposed system outperforms both of the baseline systems with a gain in accuracy of 3.6% and 5.8%. The results suggest the best feature set for the proposed system is MFCCs with VAD. We find that, on the application of VAD, the spectrograms and MFCC feature vectors become of variable size. This is because VAD filters noisy frames from speech signals, with only speech frames filtered out. To handle the variable length of the feature vectors, i.e., to make them fixed length, we perform zero padding using Algorithm 1. One of the effects of using VAD before feature calculation is that it reduces the dimensionality of the features (16,281 and 11,457 spectrogram features, and 3240 vs. 2280 MFCC features), which, in turn, decreases the step size, as shown in Table 4. The model loss during training with different features is presented in Figure 9. It can be observed that, when using spectrograms, the model converges more slowly compared to during the use of MFCC features. We also notice that the MLP model converges earlier in terms of training loss compared to the LSTM and BiLSTM models. The loss declines sharply after just a few epochs, which supports the idea that the MLP model rapidly learns the input data weights. LSTM and BiLSTM take longer to converge due to their complexity and the need to handle sequential data input dependencies. Additionally, the figure shows that, with the application of VAD, the noise interference in the MLP model is minimized, resulting in a smoother loss curve. The validation loss shown in Figure 10 also indicates that MFCC features with VAD outperform both MFCC features without VAD and spectrogram features. The validation loss curves for the MLP, LSTM, and BiLSTM models are displayed. In this experiment, the MLP consistently outperforms both LSTM and BiLSTM across all epochs, maintaining a lower validation loss. This suggests that the MLP is more likely to generalize well to unseen datasets compared to LSTM and BiLSTM, as the latter models exhibit higher fluctuations in validation loss. This is particularly true when MFCC features are combined with VAD, as VAD helps stabilize the validation performance of the MLP model. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**MFCC Features** & **Without VAD** & **With VAD** & **Spectrogram Features** & **With VAD** \\ \hline  & 8 s 22 ms/step & 3 s 6 ms/step & 16 s 31 ms/step & 11 s 21 ms/step \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average step size reduction due to VAD. Figure 9: MLP model loss with different features. The model accuracy is shown in Figure 11. The MLP model achieves the highest training accuracy, surpassing both LSTM and BiLSTM. The prominent features learned by the MLP model contribute to a steeper rise in accuracy. Although the accuracy of LSTM and BiLSTM improves over the epochs, it remains slightly below that of the MLP model, indicating that these models require more epochs to capture sequential information but still do not reach the accuracy level of MLP. This figure reinforces the idea that using VAD with MFCCs improves model performance, particularly in noisy environments. The validation accuracy is shown in Figure 12. As depicted, while the LSTM and BiLSTM models have the lowest validation accuracy, the MLP model performs significantly better. The consistency of the validation accuracy curve for the MLP suggests that it can predict speakers in the test data more reliably. Although LSTM and BiLSTM show steady improvements in accuracy, they experience greater intra-iteration fluctuations compared to the embedding-RNN case, and their rate of accuracy improvement is relatively slower.