Reducing footprint of unit selection TTS system by removing linguistic segments with rarely selected units

This paper is focused on reducing the size of speech corpora that are used in the unit-selection-based TTS systems. The size of a speech corpus influences the system requirements like storage and memory demands and computational complexity. For high quality speech synthesis, the speech corpus usually consists of several thousands of sentences. Thus an appropriate reduction of the corpus size is likely to lead to a decrease in the system requirements. In this work, a comparison of impacts on synthetic speech quality is presented when removing specific instances of different linguistic segment types from the original corpus. Removal of the following segment types is used and compared with each other: whole sentences, phrases, words, and diphones. Only segments with rarely selected units are removed from the corpus so that the resulting footprint size reaches a predefined value. Results confirm that synthetic speech generated by the TTS systems using the reduced corpora is of a slightly worse quality when compared with speech produced by the system employing the original full corpus. The comparison of the reduction based on different linguistic segments is also presented here.