Several methods have recently been proposed to analyze speech and automatically infer the personality of the speaker. These methods often rely on prosodic and other hand crafted speech processing features extracted with off-the-shelf toolboxes. To achieve high accuracy, numerous features are typically extracted using complex and highly parameterized algorithms. In this paper, a new method based on feature learning and spectrogram analysis is proposed to simplify the feature extraction process while maintaining a high level of accuracy. The proposed method learns a dictionary of discriminant features from patches extracted in the spectrogram representations of training speech segments. Each speech segment is then encoded using the dictionary, and the resulting feature set is used to perform classification of personality traits. Experiments indicate that the proposed method achieves state-of-the-art results with an important reduction in complexity when compared to the most recent reference methods. The number of features, and difficulties linked to the feature extraction process are greatly reduced as only one type of descriptors is used, for which the 7 parameters can be tuned automatically. In contrast, the simplest reference method uses 4 types of descriptors to which 6 functionals are applied, resulting in over 20 parameters to be tuned.

中文翻译：

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用于评估人格特质的频谱图特征学习

最近提出了几种方法来分析语音并自动推断说话者的个性。这些方法通常依赖于使用现成工具箱提取的韵律和其他手工制作的语音处理特征。为了实现高精度，通常使用复杂且高度参数化的算法提取大量特征。在本文中，提出了一种基于特征学习和频谱图分析的新方法，以简化特征提取过程，同时保持较高的精度。所提出的方法从训练语音片段的频谱图表示中提取的补丁中学习判别特征字典。然后使用字典对每个语音片段进行编码，并使用生成的特征集对个性特征进行分类。实验表明，与最新的参考方法相比，所提出的方法实现了最先进的结果，并显着降低了复杂性。由于仅使用一种类型的描述符，因此可以自动调整 7 个参数，从而大大减少了与特征提取过程相关的特征数量和困难。相比之下，最简单的参考方法使用 4 种类型的描述符，应用了 6 个函数，导致需要调整 20 多个参数。7个参数可以自动调整。相比之下，最简单的参考方法使用 4 种类型的描述符，应用了 6 个函数，导致需要调整 20 多个参数。7个参数可以自动调整。相比之下，最简单的参考方法使用 4 种类型的描述符，应用了 6 个函数，导致需要调整 20 多个参数。