Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning
Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning
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DOI：
10.1016/j.ymeth.2018.07.007
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发表时间：
2018-12-01
期刊：
影响因子：
4.8
通讯作者：
Schuller, Bjoern W.
中科院分区：
文献类型：
--
作者：
Cummins, Nicholas;Baird, Alice;Schuller, Bjoern W.
由于与它们的产生相关联的复杂和错综复杂的性质，语音信号的声学韵律属性被调制有一系列健康相关的影响。在这个语音和健康领域，机器学习研究领域非常活跃且不断增长，重点是开发范式来客观地提取和测量这些影响。同时，深度学习正在改变智能信号分析，使得机器现在在一系列识别和分析任务中接近人类的能力。在这里，我们回顾了当前基于语音的健康检测的最新方法，特别关注深度学习在这一领域的影响。基于这一概述，很明显，虽然基于深度学习的解决方案在文献中越来越多，但它并没有在其他相关领域中看到同样的整体主导作用。在这方面，我们提出了一些可能的研究方向，旨在充分利用深度学习可以提供基于语音的健康检测的优势。
Due to the complex and intricate nature associated with their production, the acoustic-prosodic properties of a speech signal are modulated with a range of health related effects. There is an active and growing area of machine learning research in this speech and health domain, focusing on developing paradigms to objectively extract and measure such effects. Concurrently, deep learning is transforming intelligent signal analysis, such that machines are now reaching near human capabilities in a range of recognition and analysis tasks. Herein, we review current state-of-the-art approaches with speech-based health detection, placing a particular focus on the impact of deep learning within this domain. Based on this overview, it is evident while that deep learning based solutions be become more present in the literature, it has not had the same overall dominating effect seen in other related fields. In this regard, we suggest some possible research directions aimed at fully leveraging the advantages that deep learning can offer speech-based health detection.