Speech synthesis is the task of generating speech from some other modality like text, lip movements etc.
Please note that the leaderboards here are not really comparable between studies - as they use mean opinion score as a metric and collect different samples from Amazon Mechnical Turk.
( Image credit: WaveNet: A generative model for raw audio )
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A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.
Ranked #4 on Speech Synthesis on North American English
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text.
Ranked #1 on Speech Synthesis on North American English
In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms.
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system.
In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e. g., pitch, energy and more accurate duration) as conditional inputs.
We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network.