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Found 23 papers, 11 papers with code
LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
The constituent samples of LibriTTS-R are identical to those of LibriTTS, with only the sound quality improved.
Translatotron 3: Speech to Speech Translation with Monolingual Data
This paper presents Translatotron 3, a novel approach to train a direct speech-to-speech translation model from monolingual speech-text datasets only in a fully unsupervised manner.
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Experiments show that Miipher (i) is robust against various audio degradation and (ii) enable us to train a high-quality text-to-speech (TTS) model from restored speech samples collected from the Web.
Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation
Adapting a neural text-to-speech (TTS) model to a target speaker typically involves fine-tuning most if not all of the parameters of a pretrained multi-speaker backbone model.
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration
The DDPMs and GANs can be characterized by the iterative denoising framework and adversarial training, respectively.
MAESTRO: Matched Speech Text Representations through Modality Matching
Self-supervised learning from speech signals aims to learn the latent structure inherent in the signal, while self-supervised learning from text attempts to capture lexical information.
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping
Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features.
CVSS Corpus and Massively Multilingual Speech-to-Speech Translation
In addition, CVSS provides normalized translation text which matches the pronunciation in the translation speech.
WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
The model takes an input phoneme sequence, and through an iterative refinement process, generates an audio waveform.
PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS
This paper introduces PnG BERT, a new encoder model for neural TTS.
Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling
This paper presents Non-Attentive Tacotron based on the Tacotron 2 text-to-speech model, replacing the attention mechanism with an explicit duration predictor.
WaveGrad: Estimating Gradients for Waveform Generation
This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density.
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior
Recent neural text-to-speech (TTS) models with fine-grained latent features enable precise control of the prosody of synthesized speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis
This paper proposes a hierarchical, fine-grained and interpretable latent variable model for prosody based on the Tacotron 2 text-to-speech model.
Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning
We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages.
LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
This paper introduces a new speech corpus called "LibriTTS" designed for text-to-speech use.
Sound Audio and Speech Processing
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Hierarchical Generative Modeling for Controllable Speech Synthesis
This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions.
Sample Efficient Adaptive Text-to-Speech
Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers.
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system.
WaveNet: A Generative Model for Raw Audio
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
Ranked #1 on
Speech Synthesis
on Mandarin Chinese
Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices
Acoustic models based on long short-term memory recurrent neural networks (LSTM-RNNs) were applied to statistical parametric speech synthesis (SPSS) and showed significant improvements in naturalness and latency over those based on hidden Markov models (HMMs).
