Speech signal processing is a cornerstone of modern communication technologies, tasked with improving the clarity and comprehensibility of audio data in noisy environments. The primary challenge in this field is the effective separation and recognition of speech from background noise, crucial for applications ranging from voice-activated assistants to automated transcription services. The quality of speech recognition directly impacts user experience and accessibility in technology-driven communication. This review paper explores advanced clustering techniques, particularly focusing on the Kernel Fuzzy C-Means (KFCM) method, to address these challenges. Our findings indicate that KFCM, compared to traditional methods like K-Means (KM) and Fuzzy C-Means (FCM), provides superior performance in handling non-linear and non-stationary noise conditions in speech signals. The most notable outcome of this review is the adaptability of KFCM to various noisy environments, making it a robust choice for speech enhancement applications. Additionally, the paper identifies gaps in current methodologies, such as the need for more dynamic clustering algorithms that can adapt in real time to changing noise conditions without compromising speech recognition quality. Key contributions include a detailed comparative analysis of current clustering algorithms and suggestions for further integrating hybrid models that combine KFCM with neural networks to enhance speech recognition accuracy. Through this review, we advocate for a shift towards more sophisticated, adaptive clustering techniques that can significantly improve speech enhancement and pave the way for more resilient speech processing systems.

翻译：语音信号处理是现代通信技术的基石，其核心任务是在噪声环境中提升音频数据的清晰度与可懂度。该领域的主要挑战在于有效分离和识别语音与背景噪声，这对从语音助手到自动转录服务的各类应用至关重要。语音识别的质量直接影响技术驱动型通信中的用户体验与可访问性。本文综述了先进聚类技术，特别聚焦于核模糊C均值方法，以应对这些挑战。研究发现，相较于K均值、模糊C均值等传统方法，核模糊C均值在处理语音信号中的非线性与非平稳噪声条件时表现出更优性能。本综述最显著的结论是核模糊C均值对不同噪声环境的适应能力，使其成为语音增强应用中稳健的选择。此外，本文指出了当前方法存在的不足，例如需要更具动态性的聚类算法，能够实时适应变化的噪声条件而不影响语音识别质量。主要贡献包括对现有聚类算法的详细对比分析，以及关于进一步整合核模糊C均值与神经网络的混合模型以提升语音识别准确性的建议。通过本综述，我们倡导转向更复杂、自适应的聚类技术，这些技术可显著改善语音增强效果，并为构建更具鲁棒性的语音处理系统奠定基础。