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End-to-end text-to-speech (TTS) synthesis is a method that directly convertsinput text to output acoustic features using a single network. A recent advanceof end-to-end TTS is due to a key technique called attention mechanisms, andall successful methods proposed so far have been based on soft attentionmechanisms. However, although network structures are becoming increasinglycomplex, end-to-end TTS systems with soft attention mechanisms may still failto learn and to predict accurate alignment between the input and output. Thismay be because the soft attention mechanisms are too flexible. Therefore, wepropose an approach that has more explicit but natural constraints suitable forspeech signals to make alignment learning and prediction of end-to-end TTSsystems more robust. The proposed system, with the constrained alignment schemeborrowed from segment-to-segment neural transduction (SSNT), directlycalculates the joint probability of acoustic features and alignment given aninput text. The alignment is designed to be hard and monotonically increase byconsidering the speech nature, and it is treated as a latent variable andmarginalized during training. During prediction, both the alignment andacoustic features can be generated from the probabilistic distributions. Theadvantages of our approach are that we can simplify many modules for the softattention and that we can train the end-to-end TTS model using a singlelikelihood function. As far as we know, our approach is the first end-to-endTTS without a soft attention mechanism.

Initial investigation of an encoder-decoder end-to-end TTS framework using marginalization of monotonic hard latent alignments