# Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System
## 2 Related Work  
Recently, using different deep learning approaches, tremendous advances have been achieved in the field of automated voice recognition (ASR) [22; 23; 24]. In this section, a complete comparison of cutting-edge strategies currently being employed with a specific emphasis on the various deep learning methods. [PERSON] et al. [25] proposed a bidirectional recurrent neural network (BRNN) with a self-organizing map (SOM)-based classification scheme for Tamil speech recognition. To begin, the input voice signal is preprocessed using the Savitzky-Golay filter to remove background noise and improve the signal. Perceptual linear predictive coefficients were split to improve the accuracy of the classification. The feature vector is shifted in measure and SOM is used to select the appropriate length of the feature vector. Finally, the Tamil numerals and words are arranged using a BRNN classifier using the fixed-length feature vector from SOM as input, known as BRNN-SOM. [PERSON] et al. [26] aimed to develop speech recognition systems and improve the interaction between the home appliance and the human by giving voice commands. Speech signals are processed by dynamic time warping (DTW) techniques and use SVM to recognize the voice with up to 97% accuracy. [PERSON] et al. [14] used deep convolution encoder and long-short-term memory (LSTM) recurrent neural networks (RNN) to recognize end-to-end speech. This process uses the connectionist temporal classification procedure while investigating the audio signals. The convolution network uses the VGG neural network architecture, which works jointly with the encoder to investigate the speech signal. Then, the memory network stores every speech signal, which improves the system performance compared to existing methods. Finally, the framework introduced is applied to the Chinese and Japanese datasets, and the system ensures a 5% to 10% error rate. [PERSON] et al. [15] recommend continual learning algorithms such as the hidden Markov model and deep learning algorithms to perform automatic speech recognition. Here, a deep learning network learns the speech features derived from the Mel-frequency coefficient approach. The learning process minimized the deviation between the original audio and the predicted audio. The trained features are further evaluated using the Markov model to improve offline mode's overall recognition accuracy. [PERSON] et al. [27] selected a time-delayed neural network to reduce the problem of limited language analysis using the Hindi speech recognition system. The Hindi speech information is collected from Mumbai people that are processed using an i-vector adapted network. The network considers time factors when investigating speech characteristics. This process reduces training time because the delay network maintains all processed speech information. Furthermore, the effective utilization of the network parameters increases the recognition accuracy up to 89.9%, which is a 4% average improvement compared to the existing methods. [PERSON] et al. [28] created a multispeaker diarization model to recognize long conversation-based speech. The method uses audio-lexical interdependency factors to learn the model for improving the word diarization process. This learning process generates a separate training setup for the diarization and ASR systems. The training setup helps identify long conversation speech with minimum effort because the data augmentation and decoding algorithm recognizes the speech accurately. [PERSON] et al. [18] suggested a speech enhancement parameter with a genetic algorithm to create the automatic speech recognition system. This study aims to improve the recognition accuracy while investigating the noisy speech signal. Here, a genetic algorithm is applied to investigate the speech parameter and the noise features are removed from the audio, which helps improve the overall ASR system. Another stream of research on ASRs focused on speech emotion recognition (SER) [29].