2026
Trainable EEG Interpolation and Structure-Sharing Dual-Path Encoders for Brain-Assisted Target Speaker Extraction
AAAI 2026technical
Brain-assisted target speaker extraction (TSE) isolates a target speaker
12 accepted papers
Brain-assisted target speaker extraction (TSE) isolates a target speaker
Multivariate time series (MTS) forecasting endeavors to anticipate the forthcoming sequence of interdependent variables through the utilization of past observations. The prevailing methodologies, relying on deep neural networks, Transformer, or information bottleneck frameworks, persist in confronti
Domain adaptive object detection (DAOD) aims to generalize an object detector trained on labeled source-domain data to a target domain without annotations, the core principle of which is source-target feature alignment. Typically, existing approaches employ adversarial learning to align the distribu…
EEG-based emotion recognition is a key technology in brain-computer interfaces. Many previous studies have applied deep learning methods to mine emotion-related features in EEG to decode emotions. However, they overlooked the importance of electrode correlations and varying brain region activation d…
As the general description of relationships between attributes, approximate functional dependencies (AFDs) almost hold for a given dataset with a few violations. Most of existing methods for AFD discover are insufficient to balance the efficiency and accuracy due to the massive search space and perm
Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain-specific f…
This paper aims to introduce a robust singing voice synthesis (SVS) system to produce very natural and realistic singing voices efficiently by leveraging the adversarial training strategy. On one hand, we designed simple but generic random area conditional discriminators to help supervise the acoust…
We propose a novel method for zero-shot cross-lingual TTS task by using multi-stream text encoder and efficient speaker representation. Specifically, a unified multi-stream text encoder that takes both advantages of Transformer and CBHG is proposed to retain multiple hypotheses about input represent…
A trajectory tiling based, hybrid TTS is revisited in this study for improving its synthesis performance. A combination of Transformer encoder and RNN based decoder architecture where two-level, at both word and Chinese phonetic alphabet letter levels, linguistic representation is exploited to gener…
In this paper, we propose a novel method for fast and efficient few-shot TTS task, which is able to disentangle linguistic and speaker representations. Specifically, an adversarial training strategy is firstly employed to wipe out speaker information from the linguistic representations. Then the spe…
A frame-unit-selection based voice conversion system proposed earlier by us is revisited here to enhance its performance in both speech naturalness and speaker similarity. Speaker independent, bilingual (Mandarin Chinese and American English) deep neural net (DNN) acoustic model’s output, frame-leve…
Although Transformer based neural end-to-end TTS model has demonstrated extreme effectiveness in capturing long-term dependencies and achieved state-of-the-art performance, it still suffers from two problems. 1) limited ability to model sequential and local structures in sequences; 2) heavily rely o…