47 citations · 96 across the 12 of their papers we have counts for
7 papers · 1 filter
Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation
Xilin Jiang, Cong Han, Yinghao Aaron Li +1
In this study, we present a simple multi-channel framework for contrastive learning (MC-SimCLR) to encode 'what' and 'where' of spatial audios. MC-SimCLR learns joint spectral and…
HiFTNet: A Fast High-Quality Neural Vocoder with Harmonic-plus-Noise Filter and Inverse Short Time Fourier Transform
Yinghao Aaron Li, Cong Han, Xilin Jiang +1
Recent advancements in speech synthesis have leveraged GAN-based networks like HiFi-GAN and BigVGAN to produce high-fidelity waveforms from mel-spectrograms. However, these network…
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs
Yinghao Aaron Li, Cong Han, Nima Mesgarani
In recent years, large-scale pre-trained speech language models (SLMs) have demonstrated remarkable advancements in various generative speech modeling applications, such as text-to…
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
Yinghao Aaron Li, Cong Han, Vinay S. Raghavan +2
In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human…
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes
Xilin Jiang, Yinghao Aaron Li, Nima Mesgarani
Lifelong audio feature extraction involves learning new sound classes incrementally, which is essential for adapting to new data distributions over time. However, optimizing the mo…
Improved Decoding of Attentional Selection in Multi-Talker Environments with Self-Supervised Learned Speech Representation
Cong Han, Vishal Choudhari, Yinghao Aaron Li +1
Auditory attention decoding (AAD) is a technique used to identify and amplify the talker that a listener is focused on in a noisy environment. This is done by comparing the listene…
Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions
Yinghao Aaron Li, Cong Han, Xilin Jiang +1
Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic pros…