Search Results for author:
Found 8 papers, 2 papers with code
An Empirical Study on L2 Accents of Cross-lingual Text-to-Speech Systems via Vowel Space
With the recent developments in cross-lingual Text-to-Speech (TTS) systems, L2 (second-language, or foreign) accent problems arise.
Avocodo: Generative Adversarial Network for Artifact-free Vocoder
Therefore, in this paper, we investigate the relationship between these artifacts and GAN-based vocoders and propose a GAN-based vocoder, called Avocodo, that allows the synthesis of high-fidelity speech with reduced artifacts.
Hierarchical and Multi-Scale Variational Autoencoder for Diverse and Natural Non-Autoregressive Text-to-Speech
This paper proposes a hierarchical and multi-scale variational autoencoder-based non-autoregressive text-to-speech model (HiMuV-TTS) to generate natural speech with diverse speaking styles.
Into-TTS : Intonation Template Based Prosody Control System
Two proposed modules are added to the end-to-end TTS framework: an intonation predictor and an intonation encoder.
Hierarchical Context-Aware Transformers for Non-Autoregressive Text to Speech
Therefore, to improve the modeling performance of the TNA-TTS model we propose a hierarchical Transformer structure-based text encoder and audio decoder that are designed to accommodate the characteristics of each module.
FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis
Methods for modeling and controlling prosody with acoustic features have been proposed for neural text-to-speech (TTS) models.
GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis
Recent advances in neural multi-speaker text-to-speech (TTS) models have enabled the generation of reasonably good speech quality with a single model and made it possible to synthesize the speech of a speaker with limited training data.
A Neural Text-to-Speech Model Utilizing Broadcast Data Mixed with Background Music
Second, the GST-TTS model with an auxiliary quality classifier is trained with the filtered speech and a small amount of clean speech.
