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Exploring feature extraction methods for infant mood classification

机译：探索婴幼儿心情分类的特点提取方法

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Speaker state recognition is an important issue to understand the human behaviour and to achieve more comprehensive speech interactive systems, and therefore has received much attention in recent years. This work addresses the automatic classification of three types of child emotions in vocalisations: neutral mood, fussing (negative mood) and crying (negative mood). Speech, in a broad sense, contains a lot of para-linguistic information that can be revealed by means of different methods for feature extraction and, in this case, these would be useful for mood detection. Here, several set of features are proposed, combined and compared with state-of-art characteristics used for speech-related tasks, and these are based on spectral information, bio-inspired ear model, auditory sparse representations with dictionaries, optimised wavelet coefficients and optimised filter bank for cepstral representation. All the experiments were performed using the Extreme Learning Machines as classifier because it is a state-of-art classifier and to achieve comparable results. The results show that by means of the proposed feature extraction methods it is possible to improve the performance provided by the baseline features. Also, different combinations of the developed feature sets were studied in order to further exploit their properties.
机译：发言者国家识别是了解人类行为和实现更全面的演讲互动系统的重要问题，因此近年来受到了很多关注。这项工作解决了声音中三种儿童情绪的自动分类：中性情绪，令人生畏（负面情绪）和哭泣（消极情绪）。在广泛的意义上讲，包含许多可以通过不同方法提取的方法揭示的段语言信息，并且在这种情况下，这些可用于情绪检测。在这里，提出了几组特征，结合并与用于语音相关任务的最先进特性相比，这些特征基于频谱信息，生物启发耳模型，具有词典的听觉稀疏表示，优化的小波系数和用于抗痉挛表示的优化过滤器银行。所有实验都是使用极端学习机作为分类器进行的，因为它是最先进的分类器，实现了可比的结果。结果表明，通过所提出的特征提取方法，可以提高基线特征提供的性能。此外，研究了开发特征集的不同组合，以进一步利用它们的性质。

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