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Found 16 papers, 10 papers with code
A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023
This paper presents our systems (denoted as T13) for the singing voice conversion challenge (SVCC) 2023.
PromptTTS++: Controlling Speaker Identity in Prompt-Based Text-to-Speech Using Natural Language Descriptions
We propose PromptTTS++, a prompt-based text-to-speech (TTS) synthesis system that allows control over speaker identity using natural language descriptions.
NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit
This paper describes the design of NNSVS, an open-source software for neural network-based singing voice synthesis research.
Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs
Neural audio super-resolution models are typically trained on low- and high-resolution audio signal pairs.
Lightweight and High-Fidelity End-to-End Text-to-Speech with Multi-Band Generation and Inverse Short-Time Fourier Transform
We propose a lightweight end-to-end text-to-speech model using multi-band generation and inverse short-time Fourier transform.
Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis
From these features, the proposed periodicity generator produces a sample-level sinusoidal source that enables the waveform decoder to accurately reproduce the pitch.
TTS-by-TTS 2: Data-selective augmentation for neural speech synthesis using ranking support vector machine with variational autoencoder
In the proposed method, we first adopt a variational autoencoder whose posterior distribution is utilized to extract latent features representing acoustic similarity between the recorded and synthetic corpora.
Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation
Because pitch-shift data augmentation enables the coverage of a variety of pitch dynamics, it greatly stabilizes training for both VC and TTS models, even when only 1, 000 utterances of the target speaker's neutral data are available.
ESPnet2-TTS: Extending the Edge of TTS Research
This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit.
Phrase break prediction with bidirectional encoder representations in Japanese text-to-speech synthesis
We propose a novel phrase break prediction method that combines implicit features extracted from a pre-trained large language model, a. k. a BERT, and explicit features extracted from BiLSTM with linguistic features.
Parallel waveform synthesis based on generative adversarial networks with voicing-aware conditional discriminators
This paper proposes voicing-aware conditional discriminators for Parallel WaveGAN-based waveform synthesis systems.
Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network
In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN).
Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram
We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network.
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Comparative Study on Transformer vs RNN in Speech Applications
Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS).
Ranked #11 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Probability density distillation with generative adversarial networks for high-quality parallel waveform generation
As this process encourages the student to model the distribution of realistic speech waveform, the perceptual quality of the synthesized speech becomes much more natural.
