An Interdisciplinary Study of A Leaders’ Voice Characteristics: Acoustical Analysis and Members’ Cognition

Abstract

The traditional roles of leaders are to influence members and motivate them to achieve shared goals in organizations. However, leaders such as top managers and chief executive officers, in practice, do not always directly meet or influence other company members. In fact, they tend to have the greatest impact on their members through formal speeches, company procedures, and the like. As such, official speech is directly related to the motivation of company employees. In an official speech, not only the contents of the speech, but also the voice characteristics of the speaker have an important influence on listeners, as the different vocal characteristics of a person can have different effects on the listener. Therefore, according to the voice characteristics of a leader, the cognition of the members may change, and, the degree to which the members are influenced and motivated will be different. This study identifies how members may perceive a speech differently according to the different voice characteristics of leaders in formal speeches. Further, different perceptions about voices will influence members' cognition of the leader, for example, in how trustworthy they appear. The study analyzed recorded speeches of leaders, and extracted features of their speaking style through digital speech signal analysis. Then, parameters were extracted and analyzed by the time domain, frequency domain, and spectrogram domain methods. We also analyzed the parameters for use in Natural Language Processing. We investigated which leader's voice characteristics had more influence on members or were more effective on them. A person's voice characteristics can be changed. Therefore, leaders who seek to influence members in formal speeches should have effective voice characteristics to motivate followers.

CNN-Based Voice Emotion Classification Model for Risk Detection

“…Previous studies are not appropriate for the analysis of dangerous situations. In this study, short-time Fourier transform (STFT) [6] and wavelet transforms [7,8] are used as effective acoustic preprocessing techniques to create a frequency power spectrum [9] as an image. These transforms are combined with a residual network (ResNet) [10] based on a convolutional neural network (CNN) [11,12], which is a typical artificial neural network algorithm for image classification.…”