The Effect of Emotions on Brand Recall by Gender Using Voice Emotion Response with Optimal Data Analysis

Abstract

Purpose-To analyses the effect of emotions obtained by oral reproduction of advertising slogans established via Voice Emotion Response software on brand recall by gender; and to show the relevance for marketing communication of combining "Human-Computer-Interaction (HCI)" with"affective computing (AC)" as part of their mission. Design/methodology/approach-A qualitative data analysis did the review of the scientific literature retrieved from Web-of-Science Core Collection (WoSCC), using CiteSpace'scientometric technique; the quantitative data analysis did the analyse of brand recall over a sample of Taiwan' participants by "optimal data analysis". Findings-Advertising effectiveness has a positive association with emotions; brand recall varies with gender; and "HCI" connected with "AC" is an emerging area of research. Research limitations/implications-The selection of articles obtained depend the terms used in WoSCC, and this study used of only five emotions. Still the richness of the data gives some compensation. Practical implications-Marketers involved with brands need a body of knowledge on which to base their marketing communication intelligence gathering and strategic planning. Originality/value-Provides exploratory research findings related to the use of automatic tools capable of mining emotions by gender in real time, that could enhance the feedback of customers toward their brands.

A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach

“…According to the acoustic properties, information regarding voice can be acquired by several acoustic factors, such as in the spectral formant frequencies and perceptual relevance frequencies [5,6]. Software for voice recognition converts analog signals to digital signals, known as analog-to-digital conversion [7]. In order to decode a signal, a computer needs a vocabulary or a dictionary of syllables, and also a way to compare the data to the signals.…”

Gender Recognition by Voice Using an Improved Self-Labeled Algorithm

“…Speech signals taken from a recorded speech can be used to acquire acoustic attributes such as duration, intensity, frequency and filtering [2]. Some applications where gender recognition can be useful are speech emotion recognition, human to machine interaction, sorting of telephone calls by gender categorization, automatic salutations, muting sounds for a gender and audio/video categorization with tagging (see [1,[3][4][5] and the references therein).…”