## 1 Introduction  
Approximately 10% of the population suffers from some degree of hearing loss (HL) (Figure 1) due to overexposure to noise--both long-term, repeated exposure to noise and one-time exposure to a powerful sound that causes damage to the auditory system [1]. According to the World Health Organization (WHO), in 2018 [2; 3], roughly 466 million people had HL., and it is estimated that over 900 million people will have hearing loss by 2050. The usage of hearing aids and amplifying devices is the most common treatment method. However, only a small percentage of potential wearers use a hearing aid due to the general problem of enhancing speech in a noisy environment. One of the major complaints from hearing aid wearers involves the devices' lack of versatility--they amplify all sounds rather than just those the wearer wants to hear [4; 5]. The recent development of commercial hearing aids with digital signal processing capabilities has allowed the development of advanced signal processing techniques to aid the hearing impaired. The perception and translation of speech in a noisy environment are difficult, even When using state-of-the-art devices [6], which necessitates the use of effective and bettermethods to benefit people with hearing difficulties [7; 8]. Multiple studies have revealed challenges in understanding the real causes and effects related to hearing aids. Recently, deep learning offers great promise for addressing these challenges, and many algorithms based on speech enhancement (SE) can be found in the literature and have attracted great attention, such as-. Wiener filter based on prior SNR estimation [9]. This study used a directional microphone and a time variant based on producing noisy mixture signals to enhance speech. However, this method has a limitation in the case of signal components coming from diverse directions, which may harm the speech signal and thus result in a significant decline in the objective speech quality measurement [10]. The spectral subtraction [11] method evaluates the noise spectrum, and it decreases the computational complexity and memory constraint exclusion of voice activity recognition. This method frequently results in an unpleasant artifact called musical noise, which makes the audio sound as though it was recorded underwater. The Karhunen Lo'eve transform (KLT) approach [12] has been successfully used for speech communication, providing the best approximation for a stochastic signal under the condition that its rank is fixed. However, since the KLT does not consider noise, its ability to suppress noise is very poor [13]. Recently, deep neural networks (DNNs) [14, 15] have attracted great attention among hearing aid researchers. The denoising autoencoder (DAE) and its deep version the DDAE were proposed in [16, 17] for noise reduction and speech enhancement. The experimental outcomes suggest that compared with the traditional speech enhancement methods, the DDAE model can effectively reduce the noise in speech, improving the speech quality and signal-to-noise ratio. [PERSON] et al. [15] used the DDAE model in cochlear implant (CI) simulation to improve the intelligibility of voice coding [18] and the actual CI receiver. [PERSON] et al. [12] introduced a separate deep autoencoder (SDAE) to estimate the clean speech and noise spectra by minimizing the total reconstruction error of the noisy speech spectrum. By adjusting the estimated clean speech spectrum and the unknown noise parameters in the DAE, one can reach a stationary point to minimize the total reconstruction error of the noisy speech spectrum. Deep recurrent neural networks (DRNNs) were proposed in [19], which added the recurrent structure of the speech denoising autoencoder (SDAE) [20] along with a discriminative term. Furthermore, a multi-modular neural network (MNN) was proposed in [11], which includes a particular speech enhancement module as an expert, with a speech autoencoder (AE) as the referee.