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Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual Target Speech Extraction
In audio-visual target speech extraction tasks, the audio modality tends to dominate, potentially overshadowing the importance of visual guidance.
Self-Supervised Disentangled Representation Learning for Robust Target Speech Extraction
However, in the task of target speech extraction, certain elements of global and local semantic information in the reference speech, which are irrelevant to speaker identity, can lead to speaker confusion within the speech extraction network.
Multi-Dimensional and Multi-Scale Modeling for Speech Separation Optimized by Discriminative Learning
Specifically, we design a new network SE-Conformer that can model audio sequences in multiple dimensions and scales, and apply it to the dual-path speech separation framework.
A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments
In noisy and reverberant environments, the performance of deep learning-based speech separation methods drops dramatically because previous methods are not designed and optimized for such situations.
Review of end-to-end speech synthesis technology based on deep learning
As an indispensable part of modern human-computer interaction system, speech synthesis technology helps users get the output of intelligent machine more easily and intuitively, thus has attracted more and more attention.
