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Found 13 papers, 4 papers with code
Training Universal Vocoders with Feature Smoothing-Based Augmentation Methods for High-Quality TTS Systems
While universal vocoders have achieved proficient waveform generation across diverse voices, their integration into text-to-speech (TTS) tasks often results in degraded synthetic quality.
Integrating Paralinguistics in Speech-Empowered Large Language Models for Natural Conversation
Recent work shows promising results in expanding the capabilities of large language models (LLM) to directly understand and synthesize speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech
To this end, the baseline TTS model needs to be amply generalized to out-of-domain data (i. e., target speaker's speech).
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.
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.
Effective parameter estimation methods for an ExcitNet model in generative text-to-speech systems
In this paper, we propose a high-quality generative text-to-speech (TTS) system using an effective spectrum and excitation estimation method.
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.
ExcitNet vocoder: A neural excitation model for parametric speech synthesis systems
Conventional WaveNet-based neural vocoding systems significantly improve the perceptual quality of synthesized speech by statistically generating a time sequence of speech waveforms through an auto-regressive framework.
Speaker-adaptive neural vocoders for parametric speech synthesis systems
To generate more natural speech signals with the constraint of limited training data, we propose a speaker adaptation task with an effective variation of neural vocoding models.
