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1.发明授权Method for additive and convolutional noise adaptation in automatic speech recognition using transformed matrices 有权使用变换矩阵的自动语音识别中的加法和卷积噪声适应的方法公开(公告)号：US06691091B1
公开(公告)日：2004-02-10
申请号：US09628376
申请日：2000-07-31
IPC分类号： G10L1506
CPC分类号： G10L15/063 , G10L15/20 , G10L21/0216
摘要： A noise adaptation system and method provide for noise adaptation in a speech recognition system. The method includes the steps of generating a reference model based on a training speech signal, and compensating the reference model for additive noise in the cepstral domain. The reference model is also compensated for convolutional noise in the cepstral domain. In one embodiment, the convolutional noise is compensated for by estimating a convolutional bias between the reference model and a target speech signal. The estimated convolutional bias is transformed with a channel adaptation matrix, and the transformed convolutional bias is added to the reference model in the cepstral domain.
摘要翻译： 噪声适应系统和方法提供语音识别系统中的噪声适应。 该方法包括以下步骤：基于训练语音信号产生参考模型，以及补偿倒谱域中加性噪声的参考模型。 参考模型也被补偿了倒谱域中的卷积噪声。 在一个实施例中，通过估计参考模型和目标语音信号之间的卷积偏差来补偿卷积噪声。 用通道自适应矩阵对估计的卷积偏差进行变换，并将变换的卷积偏差加到倒谱域中的参考模型中。
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2.发明授权Method for noise adaptation in automatic speech recognition using transformed matrices 有权使用变换矩阵的自动语音识别中的噪声适应方法公开(公告)号：US06529872B1
公开(公告)日：2003-03-04
申请号：US09551001
申请日：2000-04-18
IPC分类号： G10L1506
CPC分类号： G10L15/063 , G10L15/20 , G10L21/0216
摘要： The improved noise adaptation technique employs a linear or non-linear transformation to the set of Jacobian matrices corresponding to an initial noise condition. An &agr;-adaptation parameter or artificial intelligence operation is employed in a linear or non-linear way to increase the adaptation bias added to the speech models. This corrects shortcomings of conventional Jacobian adaptation, which tend to underestimate the effect of noise. The improved adaptation technique is further enhanced by a reduced dimensionality, principal component analysis technique that reduces the computational burden, making the adaptation technique beneficial in embedded recognition systems.
摘要翻译： 改进的噪声适应技术对与初始噪声条件相对应的雅可比矩阵集合采用线性或非线性变换。 以线性或非线性方式采用阿尔法适应参数或人工智能操作，以增加添加到语音模型中的适应偏差。 这纠正了常规雅各布适应的缺点，这倾向于低估噪声的影响。 改进的适应技术通过降低维度的主要成分分析技术进一步增强，主要成分分析技术降低了计算负担，使得适应技术在嵌入式识别系统中有益。
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