10 citations · 15 across the 6 of their papers we have counts for
6 papers
Unlocking Foundation Models for Privacy-Enhancing Speech Understanding: An Early Study on Low Resource Speech Training Leveraging Label-guided Synthetic Speech Content
Tiantian Feng, Digbalay Bose, Xuan Shi +1
Automatic Speech Understanding (ASU) leverages the power of deep learning models for accurate interpretation of human speech, leading to a wide range of speech applications that en…
Can Knowledge of End-to-End Text-to-Speech Models Improve Neural MIDI-to-Audio Synthesis Systems?
Xuan Shi, Erica Cooper, Xin Wang +2
With the similarity between music and speech synthesis from symbolic input and the rapid development of text-to-speech (TTS) techniques, it is worthwhile to explore ways to improve…
Use of speaker recognition approaches for learning and evaluating embedding representations of musical instrument sounds
Xuan Shi, Erica Cooper, Junichi Yamagishi
Constructing an embedding space for musical instrument sounds that can meaningfully represent new and unseen instruments is important for downstream music generation tasks such as…
RepGN:Object Detection with Relational Proposal Graph Network
Xingjian Du, Xuan Shi, Risheng Huang
Region based object detectors achieve the state-of-the-art performance, but few consider to model the relation of proposals. In this paper, we explore the idea of modeling the rela…
End-to-End Model for Speech Enhancement by Consistent Spectrogram Masking
Xingjian Du, Mengyao Zhu, Xuan Shi +3
Recently, phase processing is attracting increasinginterest in speech enhancement community. Some researchersintegrate phase estimations module into speech enhancementmodels by usi…
End-to-End Residual CNN with L-GM Loss Speaker Verification System
Xuan Shi, Xingjian Du, Mengyao Zhu
We propose an end-to-end speaker verification system based on the neural network and trained by a loss function with less computational complexity. The end-to-end speaker verificat…