Cultural dependency analysis for understanding speech emotion

The influence of language and culture on the understanding of vocal emotions

“…For example, everyday joy is a pleasant rather than exalted feeling, anger is hardly expressed as rage, but rather as irony or resentment, and sadness is usually expressed as anxiety rather than despair. Therefore we can say that universality applies only to the classification of emotions into positive and negative, whereas the expression and understanding of emotions depend on culture and language (Altrov 2013, Altrov and Pajupuu 2010, Elfenbein 2013, Kamaruddin et al 2012, and Pell et al 2009). …”

Measuring Customer Perceptions Index Using CMAC Speech Emotion Mapping

“…The NTU-American [9,10] and The Berlin Emo-db [11] datasets representing American and European cultural influence respectively. Further explanations of the datasets are reported in [9,10].…”

“…To date, no researchers can boast highest performance recorded due to the complexity of speech emotion recognition task. It is very much dependent on many factors, such as: the data itself, number of emotion identified and cultural influence [10].…”

“…In addition, neutral is also taken into consideration that serves as the emotionless state. The NTU_American [9,10] and Berlin Emo-db [11] data corpus will be used to test the feasibility of our proposed approach.…”

A Retrospective Assessment of Fuzzy Logic Applications in Voice Communications and Speech Analytics

“…There are exceptions from the mechanistic approach to assessing the speaker state; such exceptions deserve recognition, e.g., [21], [22], [23], who study correlations between qualitative representations of emotions such as valence, activation, and dominance and the acousto-physical parameters (acoustic features). On the other hand, one has to recognize the market value of the mechanistic approaches in analytics and other applications: they aim to produce real-life applications such as synthesizing emotional speech for the games and movies industry [40], monitoring the state of drivers [26], [27], [28], call center control and crowd/social state monitoring and control. There was little research on differentiating simulated emotions with variable degrees of likeness to the true ones.…”