7 citations · 9 across the 7 of their papers we have counts for
7 papers
Preserving Speech-to-Text LLM Capabilities in Speech-to-Speech Generation
Yuxuan Hu, Heng Lu, Ruchao Fan +8
Strong speech-to-text (S2T) LLMs already provide robust speech perception and text reasoning, but adding speech-to-speech (S2S) output is challenging: fine-tuning the backbone can…
Combined Generative and Predictive Modeling for Speech Super-resolution
Heming Wang, Eric W. Healy, DeLiang Wang
Speech super-resolution (SR) is the task that restores high-resolution speech from low-resolution input. Existing models employ simulated data and constrained experimental settings…
Leveraging Laryngograph Data for Robust Voicing Detection in Speech
Yixuan Zhang, Heming Wang, DeLiang Wang
Accurately detecting voiced intervals in speech signals is a critical step in pitch tracking and has numerous applications. While conventional signal processing methods and deep le…
DDTSE: Discriminative Diffusion Model for Target Speech Extraction
Leying Zhang, Yao Qian, Linfeng Yu +5
Diffusion models have gained attention in speech enhancement tasks, providing an alternative to conventional discriminative methods. However, research on target speech extraction u…
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
Heming Wang, Yao Qian, Xiaofei Wang +6
Noise robustness is essential for deploying automatic speech recognition (ASR) systems in real-world environments. One way to reduce the effect of noise interference is to employ a…
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition
Yiming Wang, Jinyu Li, Heming Wang +3
The goal of self-supervised learning (SSL) for automatic speech recognition (ASR) is to learn good speech representations from a large amount of unlabeled speech for the downstream…
A Novel Foward-PDE Approach as an Alternative to Empirical Mode Decomposition
Heming Wang, Richard Mann, Edward R. Vrscay
In this paper we present a mathematical model of the Empirical Mode Decomposition (EMD). Although EMD is a powerful tool for signal processing, the algorithm itself lacks an approp…