Paper Info
Title
Tacotron-Based Acoustic Model Using Phoneme Alignment for Practical Neural Text-to-Speech Systems
Abstract
Although sequence-to-sequence (seq2seq) models with attention mechanism in neural text-to-speech (TTS) systems, such as Tacotron 2, can jointly optimize duration and acoustic models, and realize high-fidelity synthesis compared with conventional duration-acoustic pipeline models, these involve a risk that speech samples cannot be sometimes successfully synthesized due to the attention prediction errors. Therefore, these seq2seq models cannot be directly introduced in practical TTS systems. On the other hand, the conventional pipeline models are broadly used in practical TTS systems since there are few crucial prediction errors in the duration model. For realizing high-quality practical TTS systems without attention prediction errors, this paper investigates Tacotron-based acoustic models with phoneme alignment instead of attention. The phoneme durations are first obtained from HMM-based forced alignment and the duration model is a simple bidirectional LSTM-based network. Then, a seq2seq model with forced alignment instead of attention is investigated and an alternative model with Tacotron decoder and phoneme duration is proposed. The results of experiments with full-context label input using WaveGlow vocoder indicate that the proposed model can realize a high-fidelity TTS system for Japanese with a real-time factor of 0.13 using a GPU without attention prediction errors compared with the seq2seq models.
Year
DOI
Venue
2019
10.1109/ASRU46091.2019.9003956
2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Keywords
DocType
ISBN
Speech synthesis,neural text-to-speech,duration model,forced alignment,sequence-to-sequence model
Conference
978-1-7281-0307-5
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Takuma Okamoto143.46
Tomoki Toda21874167.18
Yoshinori Shiga34513.35
Hisashi Kawai425054.04