Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis
This addresses the inefficiency and complexity in TTS systems by enabling end-to-end training and faster synthesis, though it is incremental as it builds on existing Tacotron and flow-based methods.
The authors tackled the problem of text-to-speech synthesis by developing a sequence-to-sequence neural network that directly generates speech waveforms from text, eliminating the need for intermediate spectrograms. The model achieved speech quality approaching state-of-the-art systems with significantly improved generation speed.
We describe a sequence-to-sequence neural network which directly generates speech waveforms from text inputs. The architecture extends the Tacotron model by incorporating a normalizing flow into the autoregressive decoder loop. Output waveforms are modeled as a sequence of non-overlapping fixed-length blocks, each one containing hundreds of samples. The interdependencies of waveform samples within each block are modeled using the normalizing flow, enabling parallel training and synthesis. Longer-term dependencies are handled autoregressively by conditioning each flow on preceding blocks.This model can be optimized directly with maximum likelihood, with-out using intermediate, hand-designed features nor additional loss terms. Contemporary state-of-the-art text-to-speech (TTS) systems use a cascade of separately learned models: one (such as Tacotron) which generates intermediate features (such as spectrograms) from text, followed by a vocoder (such as WaveRNN) which generates waveform samples from the intermediate features. The proposed system, in contrast, does not use a fixed intermediate representation, and learns all parameters end-to-end. Experiments show that the proposed model generates speech with quality approaching a state-of-the-art neural TTS system, with significantly improved generation speed.