  Recent studies have introduced end-to-end TTS, which integrates the
production of context and acoustic features in statistical parametric speech
synthesis. As a result, a single neural network replaced laborious feature
engineering with automated feature learning. However, little is known about
what types of context information end-to-end TTS extracts from text input
before synthesizing speech, and the previous knowledge about context features
is barely utilized. In this work, we first point out the model similarity
between end-to-end TTS and parametric TTS. Based on the similarity, we evaluate
the quality of encoder outputs from an end-to-end TTS system against eight
criteria that are derived from a standard set of context information used in
parametric TTS. We conduct experiments using an evaluation procedure that has
been newly developed in the machine learning literature for quantitative
analysis of neural representations, while adapting it to the TTS domain.
Experimental results show that the encoder outputs reflect both linguistic and
phonetic contexts, such as vowel reduction at phoneme level, lexical stress at
syllable level, and part-of-speech at word level, possibly due to the joint
optimization of context and acoustic features.
