GTM: the generative topographic mapping
Neural Computation
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Unit selection in a concatenative speech synthesis system using a large speech database
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Graph-theoretic measure for active iGAs: interaction sizing and parallel evaluation ensemble
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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Unit-selection based Text-to-Speech synthesis systems aim to obtain high quality synthetic speech by optimally selecting previously recorded units. To that effect these units are selected by a dynamic programming algorithm guided through a weighted cost function. Thus, in this context, weights should be tuned perceptually so as to be in agreement with perception from listening users. In previous works we have proposed to subjectively tune these weights through an interactive evolutionary process, also known as Active Interactive Genetic Algorithm (aiGA). The problem comes out when different users, although being consistent, evolve to different weight configurations. In this proof-of-principle work, Generative Topographic Mapping (GTM) is introduced as a method to extract knowledge from user specific preferences. The experiments show that GTM is able to capture user preferences, thus, avoiding selecting the best evolved weight configuration by means of a second preference test.