论文标题
一种新的非线性扬声器参数化算法，用于扬声器识别
A New Nonlinear speaker parameterization algorithm for speaker identification
论文作者
论文摘要
在本文中，我们根据非线性预测提出了一种新的参数化算法，该算法是经典LPC参数的扩展。参数性能通过两种不同的方法估算：算术谐波球（AHS）和自动回归矢量模型（ARVM）。提出了基于神经预测编码（NPC）的参数化的两种不同方法：经典神经网络初始化和线性初始化。我们将这两个参数应用于说话者识别。拳头参数获得的速率较小。我们显示了第一个参数如何将它们与经典参数（LPCC，MFCC等）结合在一起，以改善仅一个经典参数的结果（MFCC提供97.55％和MFCC+NPC 98.78％）。对于线性初始化，我们获得了100％，这是一个很好的改进。这项研究为不同的参数化方案开辟了一种新的方式，该方案可在说话者识别任务方面具有更好的准确性。
In this paper we propose a new parameterization algorithm based on nonlinear prediction, which is an extension of the classical LPC parameters. The parameters performances are estimated by two different methods: the Arithmetic-Harmonic Sphericity (AHS) and the Auto-Regressive Vector Model (ARVM). Two different methods are proposed for the parameterization based on the Neural Predictive Coding (NPC): classical neural networks initialization and linear initialization. We applied these two parameters to speaker identification. The fist parameters obtained smaller rates. We show for the first parameters how they can be combined with the classical parameters (LPCC, MFCC, etc.) in order to improve the results of only one classical parameterization (MFCC provides 97.55% and MFCC+NPC 98.78%). For the linear initialization, we obtain 100% which is great improvement. This study opens a new way towards different parameterization schemes that offer better accuracy on speaker recognition tasks.
