Search Results for author:
Found 5 papers, 3 papers with code
A Teacher-student Framework for Unsupervised Speech Enhancement Using Noise Remixing Training and Two-stage Inference
The lack of clean speech is a practical challenge to the development of speech enhancement systems, which means that the training of neural network models must be done in an unsupervised manner, and there is an inevitable mismatch between their training criterion and evaluation metric.
CasNet: Investigating Channel Robustness for Speech Separation
In this study, inheriting the use of our previously constructed TAT-2mix corpus, we address the channel mismatch problem by proposing a channel-aware audio separation network (CasNet), a deep learning framework for end-to-end time-domain speech separation.
Speech-enhanced and Noise-aware Networks for Robust Speech Recognition
In this paper, a noise-aware training framework based on two cascaded neural structures is proposed to jointly optimize speech enhancement and speech recognition.
Chain-based Discriminative Autoencoders for Speech Recognition
For application to robust speech recognition, we further extend c-DcAE to hierarchical and parallel structures, resulting in hc-DcAE and pc-DcAE.
AlloST: Low-resource Speech Translation without Source Transcription
In this study, we survey methods to improve ST performance without using source transcription, and propose a learning framework that utilizes a language-independent universal phone recognizer.
