# Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System
## 4 Results and Discussion
### Subjective Performance Evaluation  
This section discusses the performance evaluation results of the HHSAE-ASR framework in a subjective manner. The dataset consists of much recorded information that is both male and female. Therefore, the testing accuracy is determined using various numbers of persons and iterations.  
Figure 6 shows that the proposed HHSAE-ASR framework attains high accuracy (98.87%) while analyzing various people's signals on a different number of iterations. The obtained results are compared to existing methods: multiobjective evolutionary optimization algorithm [12] (66.76%), deep convolution encoder and long short term recurrent neural networks [14] (73.43%), continual learning algorithms [15] (78.31%), enhancement parameter with a genetic algorithm [18] (81.34%), and MFCC and DTW [20] (93.23%).  
Table 2 illustrates the excellency of the introduced system's efficiency while investigating a different number of participants. The system examined each person's speech signal as it compared the speech word, length, and sequence-related probability value. The Markov chain rules developed according to the acoustic model, lexicon model, and language model, which helps to identify the speech relationships and their deviations in the loud and noisy environment.  
Figure 5: Training accuracy; (**a**) Training accuracy vs number of persons; (**b**) accuracy against number of iterations.  
Thus, the proposed HHSAE-ASR system recognizes the speech synthesis with 99.31% precision, 99.22% recall, 99.21% of MCC, and 99.18% of F-measure value.  
Table 2 illustrated the excellence of the introduced system's efficiency while investigating a different number of participants. It analyzes each person's speech signal based on their word length, sequence-related probability, and the chain rules that are taken between 100 to 1000 participants. The method predicts the sequence of features \(P(X|W)\) and respective \(\underset{W\in\mathbb{P}^{*}}{\sum}P(X|S),\ P(S|W)P(W)\) values help to match the training and testing features.  
Further, the method updates the network parameters in different conditions \(|E|\geq 1\) which consider the rabbit's every movement, lower and upper boundary \(X_{rand}(t)-r_{1}|X_{rand}(t)-2r_{2}X(t)|;\ q\geq 0.5\), and \((X_{rabbit}(t)-X_{m}(t))-r_{3}(LB+r_{4}(UB-LB));\ q<0.5\) conditions. Not only this, but the system uses the \(|E|<1(r\geq 0.5\ and\ |E|\geq 0.5)\) and \(|E|<1(r<0.5\ and\ |E|\geq 0.5)\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Participants**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & \multicolumn{2}{c}{**Mathew Correlation**} & \multirow{2}{*}{**F-Measure (\%)**} \\  & & & & **Coefficient (MCC) (\%  
The new system's efficiency improves when tested with various participants. It analyzes each person's speech signal based on their word length, sequence-related probability, and chain rules. The approach predicts the sequence of features and their respective values, which helps to match the training and testing features.  
In the HHSAE-ASR framework, speech patterns are continuously used to train the system. The encoder network is then fine-tuned using metaheuristic techniques to reduce the error rate classification problem to a minimum. The ASR accuracy, robustness, and dependability are enhanced by using sequence speech patterns, learning concepts, and network parameter updating.