Browsing by Author "Suviranta, Rosa"

This study is a preliminary study to verify how well a Conditioned Convolutional Variational Autoencoder (CCVAE) learns the prosodic characteristics of interaction between the Lombard effect and different focus conditions. Lombard speech is an adaptation to ambient noise manifested by rising vocal intensity, fundamental frequency, and duration. Focus marks new propositional information and is signalled by making the focused word more prominent in relation to others. A CCVAE was trained on the f0 contours and speech envelopes of a Lombard speech corpus of Finnish utterances. The model’s capability to reconstruct the prosodic charac- teristics was statistically evaluated based on bottleneck representations alone. The following questions were addressed: the appropriate size of the bottleneck layer for the task, the ability of the bottleneck representations to capture the prosodic characteris- tics and the encoding of the bottleneck representations. The study shows promising results. The method can elicit representations that can quantify prosodic effects of the underlying influences and interactions. The study found that even the low dimensional bottlenecks can conceptualise and consis- tently typologize the prosodic events of interest. However, finding the optimal bottleneck dimension still needs more research. Subsequently, the model’s ability to capture the prosodic characteristics was verified by investigating the generated samples. Based on the results, the CCVAE can capture prosodic events. The quality of the reconstruction is positively correlated with the bottleneck dimension. Finally, the encoding of the bottlenecks were examined. The CCVAE encodes the bottleneck representations similarly regardless of the training instance or the bottleneck dimension. The Lombard effect was most efficiently captured and focus conditions as second.