Speech Enhancement: A Survey of Approaches and Applications

The paper provides a comprehensive overview of speech enhancement techniques and their applications. It discusses challenges in non-stationary noise, reverberation, and overlapping speech. Approaches like comb filtering, LPC-based filtering, and adaptive filtering, HMM filtering, Wiener filtering, ML estimation, Bayesian estimation, MMSE estimation, and transform domain methods, AI-based approaches are explored. The effectiveness and challenges of each approach are discussed. Applications in telecommunications, voice-controlled systems, hearing aids, speech recognition, and audio restoration are highlighted. The paper presents outcomes and advancements in speech enhancement. Valuable insights are provided for researchers, engineers, and practitioners in the field. The findings aid in selecting suitable techniques for improved speech quality and intelligibility.