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首页> 外文期刊>ACM transactions on intelligent systems >Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
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Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

机译：深度学习对环境的鲁棒性语音识别：最新进展概述

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摘要

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition but still remains an important challenge. Data-driven supervised approaches, especially the ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks. In the meanwhile, we discuss the pros and cons of these approaches and provide their experimental results on benchmark databases. We expect that this overview can facilitate the development of the robustness of speech recognition systems in acoustic noisy environments.
机译：消除非平稳环境噪声的负面影响是自动语音识别的长期研究课题，但仍然是一个重要的挑战。数据驱动的监督方法，尤其是基于深度神经网络的方法，最近已成为传统无监督方法的潜在替代方法，并且经过充分培训，可以缓解无监督方法在各种现实声学环境中的缺点。有鉴于此，我们回顾了最近开发的，具有代表性的深度学习方法，以解决语音的非平稳加性和卷积退化，旨在为那些参与开发环境稳健的语音识别系统的人员提供指导。我们分别讨论为语音识别系统的前端和后端以及联合的前端和后端培训框架开发的单通道和多通道技术。同时，我们讨论了这些方法的优缺点，并在基准数据库上提供了它们的实验结果。我们希望本概述可以促进在嘈杂的环境中语音识别系统的鲁棒性发展。

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