论文标题
ADPCM方案中的非线性预测模型计算
Nonlinear predictive models computation in ADPCM schemes
论文作者
论文摘要
最近，已经发表了有关语音编码的非线性预测的几篇论文。在ICASSP98，我们提出了一个基于ADPCM方案的系统，其基于神经网的非线性预测因子。最关键的参数是训练程序，以实现良好的概括能力和鲁棒性，以应对训练和测试条件之间的不匹配。在本文中，我们提出了几种新方法，以提高原始系统在SEGSNR的1.2dB中的性能（使用贝叶斯正则化）。框架之间的SEGSNR的方差也被最小化，因此新方案产生了更稳定的输出质量。
Recently several papers have been published on nonlinear prediction applied to speech coding. At ICASSP98 we presented a system based on an ADPCM scheme with a nonlinear predictor based on a neural net. The most critical parameter was the training procedure in order to achieve good generalization capability and robustness against mismatch between training and testing conditions. In this paper, we propose several new approaches that improve the performance of the original system in up to 1.2dB of SEGSNR (using bayesian regularization). The variance of the SEGSNR between frames is also minimized, so the new scheme produces a more stable quality of the output.
