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Found 13 papers, 5 papers with code
A Multi-Stage Multi-Codebook VQ-VAE Approach to High-Performance Neural TTS
A vector-quantized, variational autoencoder (VQ-VAE) based feature analyzer is used to encode Mel spectrograms of speech training data by down-sampling progressively in multiple stages into MSMC Representations (MSMCRs) with different time resolutions, and quantizing them with multiple VQ codebooks, respectively.
ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS
To alleviate the difficulty in training, we propose to model linguistic and prosodic information by considering cross-sentence, embedded structure in training.
Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge
End-to-end TTS requires a large amount of speech/text paired data to cover all necessary knowledge, particularly how to pronounce different words in diverse contexts, so that a neural model may learn such knowledge accordingly.
Speech BERT Embedding For Improving Prosody in Neural TTS
Experimental results obtained by the Transformer TTS show that the proposed BERT can extract fine-grained, segment-level prosody, which is complementary to utterance-level prosody to improve the final prosody of the TTS speech.
Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis
To scale neural speech synthesis to various real-world languages, we present a multilingual end-to-end framework that maps byte inputs to spectrograms, thus allowing arbitrary input scripts.
Forward-Backward Decoding for Regularizing End-to-End TTS
Experimental results show our proposed methods especially the second one (bidirectional decoder regularization), leads a significantly improvement on both robustness and overall naturalness, as outperforming baseline (the revised version of Tacotron2) with a MOS gap of 0. 14 in a challenging test, and achieving close to human quality (4. 42 vs. 4. 49 in MOS) on general test.
Exploiting Syntactic Features in a Parsed Tree to Improve End-to-End TTS
The end-to-end TTS, which can predict speech directly from a given sequence of graphemes or phonemes, has shown improved performance over the conventional TTS.
A New GAN-based End-to-End TTS Training Algorithm
However, the autoregressive module training is affected by the exposure bias, or the mismatch between the different distributions of real and predicted data.
Feature reinforcement with word embedding and parsing information in neural TTS
In this paper, we propose a feature reinforcement method under the sequence-to-sequence neural text-to-speech (TTS) synthesis framework.
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e. g. speech utterances or handwritten documents.
Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e. g. speech utterances or handwritten documents.
