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The ability to discern an individual’s level of sincerity varies from person to person and across cultures. Sincerity is typically a key indication of personality traits such as trustworthiness, and portraying sincerity can be integral to an abundance of scenarios, e. g. , when apologising. Speech signals are one important factor when discerning sincerity and, with more modern interactions occurring remotely, automatic approaches for the recognition of sincerity from speech are beneficial during both interpersonal and professional scenarios. In this study we present details of the Sincere Apology Corpus (SINA-C ). Annotated by 22 individuals for their perception of sincerity, SINA-C is an English acted-speech corpus of 32 speakers, apologising in multiple ways. To provide an updated baseline for the corpus, various machine learning experiments are conducted. Finding that extracting deep data-representations (utilising the DEEP SPECTRUM toolkit) from the speech signals is best suited. Classification results on the binary (sincere / not sincere) task are at best 79.2 % Unweighted Average Recall and for regression, in regards to the degree of sincerity, a Root Mean Square Error of 0.395 from the standardised range [-1.51; 1.72] is obtained.

Sincerity in Acted Speech: Presenting the Sincere Apology Corpus and Results