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GTM User Modeling for aIGA Weight Tuning in TTS Synthesis
Abstract
Unit Selection Text-to-Speech Synthesis (US-TTS) systems produce synthetic speech based on the retrieval of previous recorded speech units from a speech database (corpus) driven by a weighted cost function (Black & Campbell, 1995). To obtain high quality synthetic speech these weights must be optimized efficiently. To that effect, in previous works, a technique was introduced for weight tuning based on evolutionary perceptual tests by means of Active Interactive Genetic Algorithms (aiGAs) (Alías, Llorà, Formiga, Sastry & Goldberg, 2006) aiGAs mine models that map subjective preferences from users by partial ordering graphs, synthetic fitness and Evolutionary Computation (EC) (Llorà, Sastry, Goldberg, Gupta & Lakshmi, 2005). Although aiGA propose an effective method to map single user preferences, as far as we know, the methodology to extract common solutions among different individual preferences (hereafter denoted as common knowledge) has not been tackled yet. Furthermore, there is an ambiguity problem to be solved when different users evolve to different weight configurations. In this review, Generative Topographic Mapping (GTM) is introduced as a method to extract common knowledge from aiGA models obtained from user preferences.
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