Further analysis of latent affective mapping for naturally expressive speech synthesis

An essential step in the generation of expressive speech synthesis is the automatic detection and classification of emotions most likely to be present in textual input. At last Interspeech, we introduced latent affective mapping, a new emotion analysis approach which leverages two separate levels of semantic information: one that encapsulates the foundations of the domain considered, and one that specifically accounts for the overall affective fabric of the language. The ensuing framework exposes the emergent relationship between these two levels in order to advantageously inform the emotion classification process. This paper presents further validation of latent affective mapping, as well as a detailed analysis of its behavior given the attendant richer emotional description. The various mapping instantiations supported compare favorably with more conventional techniques based on expert knowledge. In particular, representative case studies point to a better approximation of the true probability distribution across the range of standard emotions.