Ning Cheng

Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation

2021

Predicting the altered acoustic frames is an effective way of self-supervised learning for speech representation.However, it is challenging to prevent the pretrained model from overfitting.In this paper, we proposed to introduce two dropout regularization methods …

Adaptive Activation Network For Low Resource Multilingual Speech Recognition

2022 · ArXiv.org

Low resource automatic speech recognition (ASR) is a useful but thorny task, since deep learning ASR models usually need huge amounts of training data. The existing models mostly established a bottleneck (BN) layer by pre-training …

Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Data

2022 · arXiv (Cornell University)

In this paper, we proposed Adapitch, a multi-speaker TTS method that makes adaptation of the supervised module with untranscribed data. We design two self supervised modules to train the text encoder and mel decoder separately …