12 citations · 19 across the 7 of their papers we have counts for
7 papers
Masked Autoencoders as Universal Speech Enhancer
Rajalaxmi Rajagopalan, Ritwik Giri, Zhiqiang Tang +1
Supervised speech enhancement methods have been very successful. However, in practical scenarios, there is a lack of clean speech, and self-supervised learning-based (SSL) speech e…
Unifying Speech Editing Detection and Content Localization via Prior-Enhanced Audio LLMs
Jun Xue, Yi Chai, Yanzhen Ren +6
Existing speech editing detection (SED) datasets are predominantly constructed using manual splicing or limited editing operations, resulting in restricted diversity and poor cover…
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data
Zhiqiang Tang, Zihan Zhong, Tong He +1
This paper studies the best practices for automatic machine learning (AutoML). While previous AutoML efforts have predominantly focused on unimodal data, the multimodal aspect rema…
Automated Tone Transcription and Clustering with Tone2Vec
Yi Yang, Yiming Wang, ZhiQiang Tang +1
Lexical tones play a crucial role in Sino-Tibetan languages. However, current phonetic fieldwork relies on manual effort, resulting in substantial time and financial costs. This is…
Learning to Generate Answers with Citations via Factual Consistency Models
Rami Aly, Zhiqiang Tang, Samson Tan +1
Large Language Models (LLMs) frequently hallucinate, impeding their reliability in mission-critical situations. One approach to address this issue is to provide citations to releva…
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
Zhiqiang Tang, Haoyang Fang, Su Zhou +5
AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM ena…