Human Perception and Automatic Detection of Speaker Personality and Likability - Influence of Modern Telecommunication Channels

当听到未知的声音时，人类倾向于对对话者的感知个性和声音可爱性进行自发的推断。听到的声音通常通过通信信道传输，例如在基于电话的语音应用中。然而，在以前的人类和自动检测人格特质和可爱性的调查中，尚未解决传输通道效应的研究。此外，关于这些扬声器特性的自动预测，尽管感知评级的连续性，二进制分类任务已主要解决。拟议的项目将研究不同设置的传输通道，如带宽，编解码器和用户界面，对人和机器的说话人个性和可爱性检测的影响。将记录拟议分析所需的德语对话语音数据。在人的方面，将采用众包方式，从大量传输的语音材料中迅速可靠地收集听众的评估。在自动方面，将考虑使用回归模型进行个性和可爱性预测，采用最先进的技术，如深度神经网络。语音质量的措施，这些扬声器特性的预测的有效性也将进行研究。这些结果将阐明哪些传输渠道可以保留决定感知个性和可爱性的声音特性，以及如何自动预测这些特性。这可以用于基于电话语音的应用中，其目的在于估计感知的说话者特性并预测随后的用户行为。

When listening to unknown voices, humans tend to make spontaneous inferences about the perceived personality and voice likability of their interlocutors. The voices heard are generally transmitted through communication channels, e.g. in telephone-based speech applications. However, the study of transmission channel effects has not yet been addressed in previous investigations of human and automatic detection of personality traits and likability. Besides, regarding the automatic prediction of these speaker characteristics, the binary classification task has principally been tackled despite the continuous nature of the perceptive ratings. The proposed project will examine the influence of transmission channels of different settings, such as bandwidth, codec and user interface, on speaker personality and likability detection by humans and machines. Conversational speech data in German, needed for the proposed analyses, will be recorded. On the human side, crowdsourcing will be employed to rapidly and reliably gather listeners' assessments from large transmitted speech material. On the automatic side, regression models will be considered for personality and likability prediction, employing state-of-the-art techniques such as deep neural networks. The validity of speech quality measures as predictors of these speaker characteristics will also be studied. The outcomes will elucidate which transmission channels can preserve the voice properties that determine the perceived personality and likability, and how these can be automatically predicted. This can be used in applications based on telephone speech which aim at estimating perceived speaker characteristics and at foreseeing subsequent user behavior.