# Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System
## 4 Results and Discussion
### Objective Performance Evaluation  
This section determines how the proposed HHSAE-ASR framework obtains the substantial results while working on the speech recognition process. The system effectiveness is evaluated using the error rate values because it is more relevant to the maximum error-rate classification problem. The resultant value of the HHSAE-ASR is compared with the existing research works such as [12, 14, 15, 18, 20]. These methods, described in more detail in Section 2, were selected because of their utilization of the optimization techniques and functions while analyzing the speech signal.  
Table 1 illustrates the error rate analysis of the proposed HHSAE-ASR framework which is compared with the existing algorithms, such as the multiobjective evolutionary optimization algorithm [12], the deep convolution encoder and long short term recurrent neural networks [14], continual learning algorithms [15], enhancement parameter with a genetic algorithm [18], and MFCC and DTW [20]. Among these methods, the HHSAE-ASR algorithm attains minimum error values (MSE--1.11, RMSE--1.087, and VUV--1.01). The training process uses different features like the acoustic, lexicon, and language model with the speech signal. These features are more helpful in making decisions according to the probability value and chain rules.  
Here, the set of speech features are analyzed by applying the encoder network that uses the different conditions while updating the network parameters.The error rate has been evaluated on different numbers of users and the obtained results are illustrated in Figure 4.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Mean Square Error** & **Root Mean Square** & **Voice/Unvoiced** \\  & **(MSE)** & **Error (RMSE)** & **(VUV) Error** \\ \hline Multiobjective evolutionary optimization algorithm [12] & 1.65 & 1.43 & 1.28 \\ Deep convolution encoder & & & \\ and LSTM-RNN [14] & 1.53 & 1.38 & 1.26 \\ Continual learning & 1.427 & 1.25 & 1.17 \\ algorithms [15] & 1.36 & 1.14 & 1.15 \\ Genetic algorithm [18] & 1.21 & 1.12 & 1.10 \\ HHSAE-ASR & 1.11 & 1.087 & 1.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training values Error rate in Speech Processing.  
Figure 4: Error rate analysis of various numbers of persons and iterations; (**a**) The comparisons of persons and error rate; (**b**) the iterations number and error rate plot.  
Figure 4 illustrates the error rate analysis of the different number of persons that participated during the speech analysis process. The effective utilization of the speech features and training parameters helps to reduce the classification error rate. The minimum error rate directly indicates the maximum recognition accuracy on the objective analysis. The obtained results are illustrated in Figure 5.  
The above results illustrate that the proposed HHSAE-ASR framework attains effective results while investigating the speech signals on a different number of iterations and persons. The recognition system's effectiveness is further examined using the testing model for a different number of persons and iterations in the subjective analysis.