Speech enhancement by perceptual filter with sequential noise parameter estimation
Speech enhancement by perceptual filter with sequential noise parameter estimation
复制标题
通过具有顺序噪声参数估计的感知滤波器进行语音增强
DOI：
10.1109/icassp.2004.1326080
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
K. Yao
中科院分区：
文献类型：
--
作者：
Te;K. Yao
我们报告了结合序列噪声估计和感知滤波的语音增强工作。序贯估计采用了序贯EM型算法的扩展。该算法利用隐马尔可夫模型(HMM)对纯净语音的统计特性进行建模，假设噪声服从高斯分布，并用一个时变均值向量(噪声参数)进行估计。该估计过程使用一个非线性函数，该函数将语音统计、噪声和噪声观测联系起来。利用估计的噪声参数，减法类型的语音增强算法可以扩展到非平稳环境。特别是，考虑到语音识别系统对语音失真的敏感性，构造了一种在噪声抑制和语音失真之间进行权衡的频率掩蔽感知滤波器。我们在非平稳噪声下的语音增强和语音识别中的实验证明，与其他语音增强算法相比，该方法在提高性能方面似乎是有希望的。
We report work on speech enhancement that combines sequential noise estimation and perceptual filtering. The sequential estimation employs an extension of the sequential EM-type algorithm. In the algorithm, statistics of clean speech are modeled by hidden Markov models (HMM) and noise is assumed to be Gaussian distributed with a time-varying mean vector (the noise parameter) to be estimated. The estimation process uses a non-linear function that relates speech statistics, noise, and noisy observation. With the estimated noise parameter, the subtraction-type algorithm for speech enhancement may be extended to non-stationary environments. In particular, a perceptual filter with frequency masking is constructed with a tradeoff between noise reduction and speech distortion considering the sensitivity of speech recognition systems to speech distortion. Our experiments in speech enhancement and speech recognition in non-stationary noise confirmed that this approach seems promising in improving performances compared to alternative speech enhancement algorithms.