Advances in Speech Enhancement: A Comprehensive Review of Noise Suppression Techniques

Abstract

Over the past several decades, numerous methods have been developed to improve the signal-to-noise ratio, perceptual quality, and intelligibility of speech. In practice, no single method is universally optimal, as each category exhibits distinct strengths and limitations under specific acoustic conditions. The proposed taxonomy classifies speech enhancement methods according to their dominant signal modeling and enhancement mechanism, ranging from the classical signal processing approaches to modern machine learning techniques and advanced hybrid frameworks. The mathematical formulation illustrates the theoretical foundations of the different approaches, providing a clear understanding of their underlying principles and the evolution of performance across successive generations of speech enhancement methods. The comparative analysis demonstrates that statistical and subspace-based approaches offer low operational complexity but show limitations under highly nonstationary acoustic conditions. Adaptive filtering, transform-domain, and speech model-based methods exploit temporal, spectral, and speech production characteristics to achieve improved noise suppression, while perceptual methods improve subjective listening quality by incorporating psychoacoustic principles, thereby providing a more natural and intelligible listening experience. Machine learning-based techniques achieve excellent performance; however, they incur higher computational complexity and substantial training requirements. More recently, hybrid approaches have integrated complementary techniques from multiple paradigms, achieving robust performance under adverse acoustic environments. Overall, this review provides a unified perspective on speech enhancement techniques, identifies their strengths, and highlights emerging research opportunities for the development of robust, efficient, and intelligent speech enhancement systems.