# Harris Hawks Sparse Auto-Encoder Networks for Automatic Speech Recognition System
###### Abstract  
Automatic speech recognition (ASR) is an effective technique that can convert human speech into text format or computer actions. ASR systems are widely used in smart appliances, smart homes, and biometric systems. Signal processing and machine learning techniques are incorporated to recognize speech. However, traditional systems have low performance due to a noisy environment. In addition to this, accents and local differences negatively affect the ASR system's performance while analyzing speech signals. A precise speech recognition system was developed to improve the system performance to overcome these issues. This paper uses speech information from jim-schwoebel voice datasets processed by Mel-frequency cepstral coefficients (MFCCs). The MFCC algorithm extracts the valuable features that are used to recognize speech. Here, a sparse auto-encoder (SAE) neural network is used to classify the model, and the hidden Markov model (HMM) is used to decide on the speech recognition. The network performance is optimized by applying the Harris Hawks optimization (HHO) algorithm to fine-tune the network parameter. The fine-tuned network can effectively recognize speech in a noisy environment. automatic speech recognition; Mel-frequency cepstral coefficients; sparse auto-encoder neural network; hidden Markov model; natural language processing; speech recognition +
Footnote †: journal: Applied Sciences  
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Voice-powered user interfaces (VUIs) [5] are integrated into ASR to solve the issue of loud and noisy environments. The VUIs and voice assistants allow users to speak to the machine and the machine converts the speech into actions. However, false interpretations and imprecisions create further complexity with vision based sign language [6]. Misinterpretpretations occur due to the understanding of sentences, words, and their relationship with human aspects. The semantic sentence understanding is also one of the main reasons for the inaccurate ASR results. Then, machine learning methods like the hidden Markov model (HMM), the support vector machine (SVM), deep neural networks (DNN), etc. [7; 8; 9] are employed to overcome the above difficulties. However, the developed ASR system has time and lack of efficiency issues. The recognition system provides for variations of voices, which requires continuous learning and training procedures. The ASR system has minimum accuracy when it receives information from speech based on loud and background noise [10; 11]. Noise is important for malware attacks in Image-based classification [12]. Especially in offices, public spaces, and urban outdoors, noise will be one of the significant challenges in speech recognition [13]. Noise occurred due to variations in locales and the accents of speaker's speech. Noise can be reduced by incorporating headphones and particular microphones. Therefore, additional computational cost and complexity are needed when using additional devices that are not desirable in the ASR system. So, the automatic ASR system has been developed by using machine learning or deep learning algorithms [14; 15] such as recurrent neural networks [16] and the encoder-decoder with an attention mechanism [17]. Speech enhancement techniques are important for maximizing the accuracy in DDos attacks in real time [18]. This system works in conjunction with signal processing, which examines each frequency and the respective speech modulations [19]. The derived information is processed using machine learning techniques that extract meaningful patterns. Finally, the system is decomposed into the training and testing phase. In the training stage, different features are utilized to train the data to recognize the exact speech obtained from various environments.