The Importance of Accurate Alignments in End-to-End Speech Synthesis
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Unit selection synthesis systems required accurate segmentation and labeling of the speech signal owing to the concatenative nature. Hidden Markov model-based speech synthesis accommodates some transcription errors, but it was later shown that accurate transcriptions yield highly intelligible speech with smaller amounts of training data. With the arrival of end-to-end (E2E) systems, it was observed that very good quality speech could be synthesised with large amounts of data. As end-to-end synthesis progressed from Tacotron to FastSpeech2, it has become imminent that features that represent prosody are important for good-quality synthesis. In particular, durations of the sub-word units are important. Variants of FastSpeech use a teacher model or forced alignments to obtain good-quality synthesis. In this paper, we focus on duration prediction, using signal processing cues in tandem with forced alignment to produce accurate phone durations during training. The current work aims to highlight the importance of accurate alignments for good-quality synthesis. An attempt is made to train the E2E systems with accurately labeled data, and compare the same with approximately labeled data.
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