【摘要】 提出了一种最大后验 (m aximum a posteriori,MAP)估计和加权近邻回归 (weighted neighbors regression,WNR)相结合的说话人自适应方法。在 MAP自适应中 ,只有自适应数据对应的模型参数可以得到调整。针对这一缺点 ,提出一种基于变换的模型插值 /平滑方法 - WNR,利用模型近邻信息和 MAP自适应结果 ,建立距离加权的回归模型 ,对没有自适应数据的模型完成模型调整。实验证明 ,该方法可以有效地提高 MAP自适应的速度。在自适应数据为 10句时 ,音节误识率降低近 15 % ;而在自适应数据为 2 5 0句时 ,误识率降低 5 0 %以上。此外 ,证明了向量域平滑 (vectorfield sm oothing,VFS)是 WNR方法的一种退化的特例更多还原

【Abstract】 This paper describes a novel speaker adaptation framework that combines the maximum a posteriori (MAP) estimation and wighted neighbor regression (WNR) methods. A great deal of adaptation data is required in MAP adaptation because only the parameters of those models with adaptation data can be updated. To alleviate this disadvantage, a technique called WNR is presented in which the parameter relationships between the speaker independent models and the speaker adaptation models are trained by applying distance weighted regression to a set of neighbor model parameters with and without MAP adaptation. The Chinese syllable recognition error is reduced nearly 15 percent with 10 adaptation utterances and more than 50 percent with 250 utterances. In addition, vector field smoothing (VFS) can be proved to be a degenerate case of WNR.更多还原