Generating prosodic attitudes in French: Data, model and evaluation
A corpus of 322 syntactically balanced sentences uttered by one speaker with six different prosodic attitudes is analysed. The syntactic and phonotactic structure of the sentences are systematically varied in order to understand how two functions can be carried out in parallel in the prosodic continuum: (1) enunciative: demarcation of constituents; (2) illocutory: speakers attitude. The statistical analysis of the corpus demonstrates that global prototypical prosodic contours characterise each attitude. Such a global encoding is consistent with gating experiments showing that attitudes can be discriminated very early in utterances. These results are discussed in relation to a morphological and superpositional model of intonation. This model proposes that the information specific to each linguistic level (structure, hierarchy of constituents, semantic and pragmatic attributes) is encoded via superposed multiparametric contours. An implementation of this model is described that automatically captures and generates these prototypical prosodic contours. This implementation consists of parallel Recurrent Neural Networks each responsible for the encoding of one linguistic level. The identification rates of attitudes for both training and test synthetic utterances are similar to those for natural stimuli. We conclude that the study of discourse-level linguistic attributes such as prosodic attitudes is a valuable paradigm for comparing intonation models.
prosodic attitudes gating experiment automatic training corpus design f0 and macrorhythm generation perceptual evaluation prosodic model prosodic movement expansion