Abstract : Speech enhancement is a field of research that aims to improve the quality of speech communication in noisy environments. In this paper, we provide an overview of the key issues and challenges in speech enhancement, including the impact of noise on speech quality, the need for effective noise reduction techniques, and the importance of evaluation metrics. We also review a range of speech enhancement techniques, including traditional signal processing methods and more recent machine learning-based approaches such as deep neural networks and convolutional neural networks. We discuss commonly used speech databases for speech enhancement research and highlight the strengths and limitations of signal processing and machine learning techniques. We also review commonly used speech enhancement metrics, including objective measures such as signal-to-noise ratio and subjective measures such as mean opinion score. Finally, we discuss potential future horizons for speech enhancement research, including the integration of multiple techniques, real-time processing, robustness to diverse acoustic conditions, multilingual speech enhancement, personalized algorithms, and the development of more sophisticated evaluation metrics. Overall, this paper provides a comprehensive overview of speech enhancement research, and highlights some of the most promising directions for future research in this field.