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Found 13 papers, 4 papers with code
ESPnet2-TTS: Extending the Edge of TTS Research
This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit.
Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings
While speaker adaptation for end-to-end speech synthesis using speaker embeddings can produce good speaker similarity for speakers seen during training, there remains a gap for zero-shot adaptation to unseen speakers.
Audio and Speech Processing
Can Speaker Augmentation Improve Multi-Speaker End-to-End TTS?
This is followed by an analysis on synthesis quality, speaker and dialect similarity, and a remark on the effectiveness of our speaker augmentation approach.
Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language
Towards end-to-end Japanese speech synthesis, we extend Tacotron to systems with self-attention to capture long-term dependencies related to pitch accents and compare their audio quality with classical pipeline systems under various conditions to show their pros and cons.
Initial investigation of an encoder-decoder end-to-end TTS framework using marginalization of monotonic hard latent alignments
The advantages of our approach are that we can simplify many modules for the soft attention and that we can train the end-to-end TTS model using a single likelihood function.
Effect of choice of probability distribution, randomness, and search methods for alignment modeling in sequence-to-sequence text-to-speech synthesis using hard alignment
Sequence-to-sequence text-to-speech (TTS) is dominated by soft-attention-based methods.
Investigation of learning abilities on linguistic features in sequence-to-sequence text-to-speech synthesis
Our experiments suggest that a) a neural sequence-to-sequence TTS system should have a sufficient number of model parameters to produce high quality speech, b) it should also use a powerful encoder when it takes characters as inputs, and c) the encoder still has a room for improvement and needs to have an improved architecture to learn supra-segmental features more appropriately.
End-to-End Text-to-Speech using Latent Duration based on VQ-VAE
Explicit duration modeling is a key to achieving robust and efficient alignment in text-to-speech synthesis (TTS).
Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis
We explore pretraining strategies including choice of base corpus with the aim of choosing the best strategy for zero-shot multi-speaker end-to-end synthesis.
Text-to-speech synthesis based on latent variable conversion using diffusion probabilistic model and variational autoencoder
We propose a TTS method based on latent variable conversion using a diffusion probabilistic model and the variational autoencoder (VAE).
Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language
To tackle the challenge of rendering correct pitch accent in Japanese end-to-end TTS, we adopt PnG~BERT, a self-supervised pretrained model in the character and phoneme domain for TTS.
Preference-based training framework for automatic speech quality assessment using deep neural network
We propose a training framework of SQA models that can be trained with only preference scores derived from pairs of MOS to improve ranking prediction.
Automatic design optimization of preference-based subjective evaluation with online learning in crowdsourcing environment
A preference-based subjective evaluation is a key method for evaluating generative media reliably.
