While speech emotion recognition (SER) has been an active research field since the last three decades, the techniques that deal with the natural environment have only emerged in the last decade. These techniques have reduced the mismatch in the distribution of the training and testing data, which occurs due to the difference in speakers, texts, languages, and recording environments between the training and testing datasets. Although a few good surveys exist for SER, they either don't cover all aspects of SER in natural environments or don't discuss the specifics in detail. This survey focuses on SER in a natural environment, discussing SER techniques for natural environment along with their advantages and disadvantages in terms of speaker, text, language, and recording environments. In the recent past, the deep learning techniques have become very popular due to minimal speech processing and enhanced accuracy. Special attention has been given to deep-learning techniques and the related issues in this survey. Recent databases, features, and feature selection algorithms for SER, which have not been discussed in the existing surveys and can be promising for SER in a natural environment, have also been discussed in this paper.

尽管语音情感识别（SER）自最近三十年来一直是活跃的研究领域，但处理自然环境的技术仅在最近十年才出现。这些技术减少了训练和测试数据分布中的不匹配，这种不匹配是由于训练和测试数据集之间的说话者，文本，语言和记录环境的差异而导致的。尽管有一些关于SER的良好调查，但是它们或者没有涵盖自然环境中SER的所有方面，也没有详细讨论具体细节。这项调查的重点是自然环境中的SER，并讨论了自然环境中的SER技术以及它们在说话者，文本，语言和录音环境方面的优缺点。在最近的过去 由于最少的语音处理和增强的准确性，深度学习技术已变得非常流行。本次调查特别关注深度学习技术和相关问题。本文还讨论了最新的SER数据库，特征和特征选择算法，这些在现有的调查中尚未讨论，并且对于自然环境中的SER可能很有希望。