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Found 10 papers, 2 papers with code
VoxGenesis: Unsupervised Discovery of Latent Speaker Manifold for Speech Synthesis
This forces the model to learn a speaker distribution disentangled from the semantic content.
Phonetic-aware speaker embedding for far-field speaker verification
The intuition is that phonetic information can preserve low-level acoustic dynamics with speaker information and thus partly compensate for the degradation due to noise and reverberation.
Contrastive Speaker Embedding With Sequential Disentanglement
Contrastive speaker embedding assumes that the contrast between the positive and negative pairs of speech segments is attributed to speaker identity only.
Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification
Contrastive self-supervised learning (CSL) for speaker verification (SV) has drawn increasing interest recently due to its ability to exploit unlabeled data.
Progression-Guided Temporal Action Detection in Videos
The framework locates actions in videos by detecting the action evolution process.
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations
Self-supervised learning (SSL) speech models such as wav2vec and HuBERT have demonstrated state-of-the-art performance on automatic speech recognition (ASR) and proved to be extremely useful in low label-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Cluster-Guided Unsupervised Domain Adaptation for Deep Speaker Embedding
Inspired by the self-training strategies that use an existing classifier to label the unlabeled data for retraining, we propose a cluster-guided UDA framework that labels the target domain data by clustering and combines the labeled source domain data and pseudo-labeled target domain data to train a speaker embedding network.
Speaker Representation Learning via Contrastive Loss with Maximal Speaker Separability
A great challenge in speaker representation learning using deep models is to design learning objectives that can enhance the discrimination of unseen speakers under unseen domains.
Discriminative Speaker Representation via Contrastive Learning with Class-Aware Attention in Angular Space
The challenges in applying contrastive learning to speaker verification (SV) are that the softmax-based contrastive loss lacks discriminative power and that the hard negative pairs can easily influence learning.
Protecting Genomic Privacy by a Sequence-Similarity Based Obfuscation Method
In the post-genomic era, large-scale personal DNA sequences are produced and collected for genetic medical diagnoses and new drug discovery, which, however, simultaneously poses serious challenges to the protection of personal genomic privacy.
