# Speech Enhancement Algorithms: A Systematic Literature Review
## 2 Background  
Speech enhancement aims to improve the quality and intelligibility of speech signals. However, achieving better speech quality does not necessarily guarantee higher intelligibility, as these two criteria are independent. Most SEAs focus on noise reduction by improving the quality of the speech signal, often at the expense of reducing intelligibility. The quality of a speech signal refers to its clarity, intelligibility, and naturalness, which determine how well a listener or automated system can perceive and understand the speech. Quality is influenced by factors such as background noise, distortion, bandwidth, and temporal continuity [30]. High-quality speech signals exhibit minimal noise, reduced artifacts, and a well-preserved frequency spectrum, ensuring both naturalness and intelligibility [31]. In speech enhancement, various algorithms aim to improve quality by suppressing noise and distortions while preserving key speech components, making the resulting signals more suitable for applications such as telecommunication, speech recognition, and assistive hearing devices. The human ear plays a vital role in speech perception, making certain sound properties essential for speech quality. The ear is most sensitive to frequencies between 1 kHz and 4 kHz, where speech intelligibility is highest; thus, speech enhancement should preserve this range [32]. The ear also follows loudness perception patterns, meaning that speech should be balanced across frequencies in order to sound natural [33]. Additionally, the auditory system can distinguish rapid sound changes, meaning that avoiding distortion of key speech cues is crucial when enhancing speech signals [34]. Finally, binaural hearing helps with spatial awareness, enabling better speech perception in noisy environments [35]. The human voice itself can sometimes be an unclean source signal, even before any external noise or distortions are introduced; for instance, this can happen due to speech disorders, medical conditions such as dysphonia or Parkinson's disease, and stuttering, which can cause irregularities in voice production [36]. Emotional states such as whispering, shouting, or crying also affect speech clarity and make it harder to process [37]. Additionally, factors such as fatigue, dehydration, and aging can alter vocal quality, leading to hoarseness or instability [38]. In multilingual settings, accents and pronunciation variations may introduce further complexity [39]. Because of these natural variations, speech enhancement systems must adapt to intrinsic distortions in the human voice while preserving its unique characteristics. Interestingly, listeners can sometimes extract more information from a noisy signal than an enhanced one, especially when listening attentively; however, prolonged exposure to noisy signals can cause discomfort, prompting the development of methods that simultaneously enhance the quality and intelligibility of speech signals [40]. Deep learning techniques such as Convolutional Neural Networks (CNNs) have been employed to analyze and process distorted speech signals. These approaches enhance speech quality while facilitating clearer and more accurate speech inputs, benefiting tasks such as automatic speech recognition as well as applications in noisy environments. The design of a speech enhancement architecture should involve multiple standards, including an appropriate speech model, addressing different noise types, intelligibility improvement, and quality enhancement. One of the most crucial factors in any design is the mitigation of artifacts, including Musical Noise (MN), which is a common artificial distortion generated during speech enhancement or noise reduction processes. MN manifests as isolated tonal or harmonic elements resembling random musical notes, which can disrupt the listening experience [9].