30 citations · 132 across the 24 of their papers we have counts for
24 papers
AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking
Xilin Jiang, Qiaolin Wang, Junkai Wu +30
Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand suc…
StyleTTS-ZS: Efficient High-Quality Zero-Shot Text-to-Speech Synthesis with Distilled Time-Varying Style Diffusion
Yinghao Aaron Li, Xilin Jiang, Cong Han +1
The rapid development of large-scale text-to-speech (TTS) models has led to significant advancements in modeling diverse speaker prosody and voices. However, these models often fac…
Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis
Xilin Jiang, Yinghao Aaron Li, Adrian Nicolas Florea +2
It is too early to conclude that Mamba is a better alternative to transformers for speech before comparing Mamba with transformers in terms of both performance and efficiency in mu…
Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation
Xilin Jiang, Cong Han, Nima Mesgarani
Transformers have been the most successful architecture for various speech modeling tasks, including speech separation. However, the self-attention mechanism in transformers with q…
Listen, Chat, and Remix: Text-Guided Soundscape Remixing for Enhanced Auditory Experience
Xilin Jiang, Cong Han, Yinghao Aaron Li +1
In daily life, we encounter a variety of sounds, both desirable and undesirable, with limited control over their presence and volume. Our work introduces "Listen, Chat, and Remix"…
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…
Unsupervised Multi-channel Separation and Adaptation
Cong Han, Kevin Wilson, Scott Wisdom +1
A key challenge in machine learning is to generalize from training data to an application domain of interest. This work generalizes the recently-proposed mixture invariant training…
Online Binaural Speech Separation of Moving Speakers With a Wavesplit Network
Cong Han, Nima Mesgarani
Binaural speech separation in real-world scenarios often involves moving speakers. Most current speech separation methods use utterance-level permutation invariant training (u-PIT)…
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…
StyleTTS-VC: One-Shot Voice Conversion by Knowledge Transfer from Style-Based TTS Models
Yinghao Aaron Li, Cong Han, Nima Mesgarani
One-shot voice conversion (VC) aims to convert speech from any source speaker to an arbitrary target speaker with only a few seconds of reference speech from the target speaker. Th…
StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis
Yinghao Aaron Li, Cong Han, Nima Mesgarani
Text-to-Speech (TTS) has recently seen great progress in synthesizing high-quality speech owing to the rapid development of parallel TTS systems, but producing speech with naturali…
Multi-Channel Speech Denoising for Machine Ears
Cong Han, E. Merve Kaya, Kyle Hoefer +2
This work describes a speech denoising system for machine ears that aims to improve speech intelligibility and the overall listening experience in noisy environments. We recorded a…
Dual-Path Modeling for Long Recording Speech Separation in Meetings
Chenda Li, Zhuo Chen, Yi Luo +6
The continuous speech separation (CSS) is a task to separate the speech sources from a long, partially overlapped recording, which involves a varying number of speakers. A straight…
Continuous Speech Separation Using Speaker Inventory for Long Multi-talker Recording
Cong Han, Yi Luo, Chenda Li +8
Leveraging additional speaker information to facilitate speech separation has received increasing attention in recent years. Recent research includes extracting target speech by us…
Group Communication with Context Codec for Lightweight Source Separation
Yi Luo, Cong Han, Nima Mesgarani
Despite the recent progress on neural network architectures for speech separation, the balance between the model size, model complexity and model performance is still an important…
Ultra-Lightweight Speech Separation via Group Communication
Yi Luo, Cong Han, Nima Mesgarani
Model size and complexity remain the biggest challenges in the deployment of speech enhancement and separation systems on low-resource devices such as earphones and hearing aids. A…
Rethinking the Separation Layers in Speech Separation Networks
Yi Luo, Zhuo Chen, Cong Han +3
Modules in all existing speech separation networks can be categorized into single-input-multi-output (SIMO) modules and single-input-single-output (SISO) modules. SIMO modules gene…
Distortion-controlled Training for End-to-end Reverberant Speech Separation with Auxiliary Autoencoding Loss
Yi Luo, Cong Han, Nima Mesgarani
The performance of speech enhancement and separation systems in anechoic environments has been significantly advanced with the recent progress in end-to-end neural network architec…
Real-time binaural speech separation with preserved spatial cues
Cong Han, Yi Luo, Nima Mesgarani
Deep learning speech separation algorithms have achieved great success in improving the quality and intelligibility of separated speech from mixed audio. Most previous methods focu…
FaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing
Yi Luo, Enea Ceolini, Cong Han +2
Beamforming has been extensively investigated for multi-channel audio processing tasks. Recently, learning-based beamforming methods, sometimes called \textit{neural beamformers},…