| Résumé |
This paper presents a large-scale similarity search of professionally acted voices for computer-aided voice casting. The proposed voice casting system explores GMM-based acoustic models and multi-label recognition of perceived para-linguistic content (speaker states and speaker traits, e.g., age/gender, voice quality, emotion) for the voice casting of professionally acted voices. First, acoustic models (universal background model, super-vector, i-vector) are constructed to model the acoustic space of voices, from which the similarity between voices can be measured directly in the acoustic space. Second, multiple binary classification of speaker traits and states is added to the acoustic models in order to represent the vocal signature of a voice, which is then used to measure the similarity between voices in the para-linguistic space. Finally, a similarity search is processed in order to determine the set of target actors that are the most similar to the voice of a source actor. In a subjective experiment conducted in the real-context of cross-language voice casting, the multi-label scoring system significantly outperforms the acoustic scoring system. This constitutes a proof of concept for the role of perceived para-linguistic categories in the perception of voice similarity. |
