标题： 语音道长度失真特征用于语音关键字检测 显示英文标题

标题： Vocal Tract Length Warped Features for Spoken Keyword Spotting

摘要： 在本文中，我们提出几种方法，将声道长度（VTL）变形特征用于语音关键词检测（KWS）。第一种方法，VTL独立的KWS，涉及训练一个使用各种变形因子的VTL特征的深度神经网络（DNN）。在训练过程中，每个周期随机选择一个特定的VTL特征，以探索VTL的变化。在测试过程中，测试话语的不同变形因子的VTL特征被评分，并以相等权重组合。第二种方法对测试话语的传统特征（没有VTL变形）进行评分。第三种方法，VTL拼接的KWS，将VTL变形特征拼接以形成高维特征用于KWS。在英语Google命令数据集上的评估表明，所提出的方法提高了KWS的准确性。 显示英文摘要

摘要： In this paper, we propose several methods that incorporate vocal tract length (VTL) warped features for spoken keyword spotting (KWS). The first method, VTL-independent KWS, involves training a single deep neural network (DNN) that utilizes VTL features with various warping factors. During training, a specific VTL feature is randomly selected per epoch, allowing the exploration of VTL variations. During testing, the VTL features with different warping factors of a test utterance are scored against the DNN and combined with equal weight. In the second method scores the conventional features of a test utterance (without VTL warping) against the DNN. The third method, VTL-concatenation KWS, concatenates VTL warped features to form high-dimensional features for KWS. Evaluations carried out on the English Google Command dataset demonstrate that the proposed methods improve the accuracy of KWS.