Search Results for author: Cong Han

Found 21 papers, 10 papers with code

Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information

no code implementations Findings (NAACL) 2022 Bowen Yang, Cong Han, Yu Li, Lei Zuo, Zhou Yu

In this paper, we propose a simple yet effective architecture comprising a pre-trained language model (PLM) and an item metadata encoder to integrate the recommendation and the dialog generation better.

Knowledge Graphs Language Modelling +2

Listen, Chat, and Edit: Text-Guided Soundscape Modification for Enhanced Auditory Experience

no code implementations6 Feb 2024 Xilin Jiang, Cong Han, Yinghao Aaron Li, Nima Mesgarani

In daily life, we encounter a variety of sounds, both desirable and undesirable, with limited control over their presence and volume.

Language Modelling Large Language Model

Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation

no code implementations27 Sep 2023 Xilin Jiang, Cong Han, Yinghao Aaron Li, Nima Mesgarani

In this study, we present a simple multi-channel framework for contrastive learning (MC-SimCLR) to encode 'what' and 'where' of spatial audios.

Contrastive Learning Data Augmentation

HiFTNet: A Fast High-Quality Neural Vocoder with Harmonic-plus-Noise Filter and Inverse Short Time Fourier Transform

no code implementations18 Sep 2023 Yinghao Aaron Li, Cong Han, Xilin Jiang, Nima Mesgarani

Subjective evaluations on LJSpeech show that our model significantly outperforms both iSTFTNet and HiFi-GAN, achieving ground-truth-level performance.

Speech Synthesis

SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs

no code implementations18 Jul 2023 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-speech synthesis, voice conversion, and speech enhancement.

Generative Adversarial Network Language Modelling +4

StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

1 code implementation NeurIPS 2023 Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, Nima Mesgarani

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-level TTS synthesis.

Speech Synthesis

Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network

1 code implementation ICCV 2023 Cong Han, Yujie Zhong, Dengjie Li, Kai Han, Lin Ma

Recently, the open-vocabulary semantic segmentation problem has attracted increasing attention and the best performing methods are based on two-stream networks: one stream for proposal mask generation and the other for segment classification using a pretrained visual-language model.

Classification Language Modelling +4

Improved Decoding of Attentional Selection in Multi-Talker Environments with Self-Supervised Learned Speech Representation

no code implementations11 Feb 2023 Cong Han, Vishal Choudhari, Yinghao Aaron Li, Nima Mesgarani

Auditory attention decoding (AAD) is a technique used to identify and amplify the talker that a listener is focused on in a noisy environment.

Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions

2 code implementations20 Jan 2023 Yinghao Aaron Li, Cong Han, Xilin Jiang, Nima Mesgarani

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 prosodic patterns.

StyleTTS-VC: One-Shot Voice Conversion by Knowledge Transfer from Style-Based TTS Models

1 code implementation29 Dec 2022 Yinghao Aaron Li, Cong Han, Nima Mesgarani

Here, we propose a novel approach to learning disentangled speech representation by transfer learning from style-based text-to-speech (TTS) models.

Data Augmentation Transfer Learning +1

Extensible Proxy for Efficient NAS

1 code implementation17 Oct 2022 Yuhong Li, Jiajie Li, Cong Han, Pan Li, JinJun Xiong, Deming Chen

(2) Efficient proxies are not extensible to multi-modality downstream tasks.

Neural Architecture Search

StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis

1 code implementation30 May 2022 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 naturalistic prosodic variations, speaking styles and emotional tones remains challenging.

Data Augmentation Self-Supervised Learning +2

Multi-Channel Speech Denoising for Machine Ears

no code implementations17 Feb 2022 Cong Han, E. Merve Kaya, Kyle Hoefer, Malcolm Slaney, Simon Carlile

This work describes a speech denoising system for machine ears that aims to improve speech intelligibility and the overall listening experience in noisy environments.

Denoising Speech Denoising

Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information

1 code implementation15 Dec 2021 Bowen Yang, Cong Han, Yu Li, Lei Zuo, Zhou Yu

The encoder learns to map item metadata to embeddings that can reflect the semantic information in the dialog context.

Language Modelling Recommendation Systems +1

Group Communication with Context Codec for Lightweight Source Separation

1 code implementation14 Dec 2020 Yi Luo, Cong Han, Nima Mesgarani

A context codec module, containing a context encoder and a context decoder, is designed as a learnable downsampling and upsampling module to decrease the length of a sequential feature processed by the separation module.

Decoder Speech Enhancement +1

Incentive Mechanism Design for ROI-constrained Auto-bidding

no code implementations4 Dec 2020 Bin Li, Xiao Yang, Daren Sun, Zhi Ji, Zhen Jiang, Cong Han, Dong Hao

Auto-bidding plays an important role in online advertising and has become a crucial tool for advertisers and advertising platforms to meet their performance objectives and optimize the efficiency of ad delivery.

Computer Science and Game Theory

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