语音压缩感知研究进展与展望

压缩感知技术，特别是语音压缩感知技术逐渐成为信号处理领域的研究热点。当前的语音压缩感知关键技术主要包括适合语音信号的稀疏分解矩阵构造，观测矩阵的选择和重构算法的设计。稀疏分解矩阵的重要代表是正交基、基于语音特性的线性预测矩阵和过完备字典。观测矩阵方面主要采用随机观测矩阵分析语音压缩感知性能；重构算法方面重点研究当观测序列或语音信号本身含有噪声时鲁棒的语音压缩感知重构算法。本文对上述语音压缩感知的3大关键技术进行了介绍和对比分析，并对语音压缩感知的应用进行了总结，最后对未来可能的研究热点进行了展望。

Compressed Speech Sensing for Research Progress and Prospect

Compressed sensing technology, especially the compressed speech sensing technology has gradually become the research hotspot in signal processing. The currently key issues of compressed speech sensing include the construction of the sparse decomposition matrix, the selection of the measurement matrix and the design of the reconstruction algorithm for speech signal. The important representatives of sparse decomposition matrix are the orthogonal basis, linear prediction matrix based on speech characteristics and overcomplete dictionary. For measurement matrix, the performance of reconstructed speech signals based on random measurement matrix is analyzed. For reconstruction algorithm, the robust reconstruction algorithms with noisy measurement or noisy speech signal are researched. In the paper, the above three kinds of compressed speech sensing technologies are introduced and compared, and the main applications of compressed speech sensing are also provided. Finally, the possible future research points of compressed speech sensing are discussed.