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Found 6 papers, 3 papers with code
DiffWave: A Versatile Diffusion Model for Audio Synthesis
In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation.
Ranked #2 on
Speech Synthesis
on LJSpeech
Parallel Neural Text-to-Speech
In this work, we first propose ParaNet, a non-autoregressive seq2seq model that converts text to spectrogram.
WaveFlow: A Compact Flow-based Model for Raw Audio
WaveFlow provides a unified view of likelihood-based models for 1-D data, including WaveNet and WaveGlow as special cases.
Ranked #9 on
Speech Synthesis
on LibriTTS
Multi-Speaker End-to-End Speech Synthesis
In this work, we extend ClariNet (Ping et al., 2019), a fully end-to-end speech synthesis model (i. e., text-to-wave), to generate high-fidelity speech from multiple speakers.
Non-Autoregressive Neural Text-to-Speech
In this work, we propose ParaNet, a non-autoregressive seq2seq model that converts text to spectrogram.
Trace norm regularization and faster inference for embedded speech recognition RNNs
We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR).
