Using continuous lexical embeddings to improve symbolic-prosody prediction in a text-to-speech front-end
The prediction of symbolic prosodic categories from text is an important, but challenging, natural-language processing task given the various ways in which an input can be realized, and the fact that knowledge about what features determine this realization is incomplete or inaccessible to the model. In this work, we look at augmenting baseline features with lexical representations that are derived from text, providing continuous embeddings of the lexicon in a lower-dimensional space. Although learned in an unsupervised fashion, such features capture semantic and syntactic properties that make them amenable for prosody prediction. We deploy various embedding models on prominence- and phrase-break prediction tasks, showing substantial gains, particularly for prominence prediction.