In this study, we aim to reduce the human effort in preparing training data for synthesizing human speech and improve the quality of synthetic speech. In spite of the learning-from-data used to train the statistical models, the construction of a statistical parametric speech synthesizer involves substantial human effort, especially when using imperfect data or working on a new language. Here, we use lightly-supervised methods for preparing the data and constructing the text-processing front end. This initial system is then iteratively improved using active learning in which feedback from users is used to disambiguate the pronunciation system in our chosen language, Malay. The data are prepared using speaker diarisation and lightly-supervised text-speech alignment. In the front end, grapheme-based units are used. The active learning used small amounts of feedback from a listener to train a classifier. We report evaluations of two systems built from high-quality studio data and lower-quality `found' data respectively and show that the intelligibility of each can be improved using active learning.
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