Abstract Speech enhancement aims at improving the quality of speech in a noisy environment, also in applications such as speech recognition systems, hearing aids, teleconferencing, etc. Researchers have suggested various techniques to improve the quality of speech signal in a noisy environment. Kalman filtering technique showed better performance and was found remarkable in removing noise components. Therefore, this proposed method employs an improved version of the Kalman filter for the removal of noise in speech signals. A Principal Component Analysis (PCA) developed Kalman filter is proposed for enhancing speech intelligibility and quality. The simulation results prove the recommended technique of enhancing speech signals. It is better in performance compared with Modulation Compressive Sensing, Compressed Sensing Frequency, Wiener filter, and Log Minimum Mean Square Error (LogMMSE) in terms of Short-Time Objective Intelligibility (STOI), Segmental Signal to Noise Ratio (SSNR) and Perceptual Evaluation of Speech Quality (PESQ) for different noise levels.

中文翻译：

---

语音增强 - 增强型主成分分析 (EPCA) 滤波器方法

摘要 语音增强旨在提高嘈杂环境中的语音质量，也在语音识别系统、助听器、电话会议等应用中。研究人员提出了各种技术来提高嘈杂环境中的语音信号质量。卡尔曼滤波技术表现出更好的性能，并且在去除噪声成分方面表现出色。因此，该提议的方法采用卡尔曼滤波器的改进版本来去除语音信号中的噪声。提出了一种主成分分析 (PCA) 开发的卡尔曼滤波器，以提高语音清晰度和质量。仿真结果证明了增强语音信号的推荐技术。性能优于调制压缩感知、压缩感知频率、维纳滤波器、