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Advanced Data Exploitation in Speech Analysis: An overview

机译:语音分析中的高级数据开发:概述

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摘要

With recent advances in machine-learning techniques for automatic speech analysis (ASA)-the computerized extraction of information from speech signals-there is a greater need for high-quality, diverse, and very large amounts of data. Such data could be game-changing in terms of ASA system accuracy and robustness, enabling the extraction of feature representations or the learning of model parameters immune to confounding factors, such as acoustic variations, unrelated to the task at hand. However, many current ASA data sets do not meet the desired properties. Instead, they are often recorded under less than ideal conditions, with the corresponding labels sparse or unreliable.
机译:随着用于自动语音分析(ASA)的机器学习技术的最新发展-从语音信号中自动提取信息,对高质量,多样化和大量数据的需求越来越大。此类数据在ASA系统的准确性和鲁棒性方面可能会改变游戏规则,从而能够提取特征表示或学习模型参数,而不受与当前任务无关的混杂因素(例如声学变化)的影响。但是,许多当前的ASA数据集不符合所需的属性。取而代之的是,它们通常是在不理想的条件下记录的,相应的标签稀疏或不可靠。

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