10 citations · 15 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…