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Found 22 papers, 5 papers with code
Label-efficient Multi-organ Segmentation Method with Diffusion Model
In this study, we present a label-efficient learning approach using a pre-trained diffusion model for multi-organ segmentation tasks in CT images.
Creating Personalized Synthetic Voices from Articulation Impaired Speech Using Augmented Reconstruction Loss
Experimental results on a real case of tongue cancer patient confirm that the synthetic voice achieves comparable articulation quality to unimpaired natural speech, while effectively maintaining the target speaker's individuality.
Efficient Black-Box Speaker Verification Model Adaptation with Reprogramming and Backend Learning
The development of deep neural networks (DNN) has significantly enhanced the performance of speaker verification (SV) systems in recent years.
SOOD: Towards Semi-Supervised Oriented Object Detection
Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years.
DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection
In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D.
A Simple Vision Transformer for Weakly Semi-supervised 3D Object Detection
Advanced 3D object detection methods usually rely on large-scale, elaborately labeled datasets to achieve good performance.
Model Compression for DNN-based Speaker Verification Using Weight Quantization
Experimental results on VoxCeleb show that weight quantization is effective for compressing SV models.
Convolution-Based Channel-Frequency Attention for Text-Independent Speaker Verification
The weights are imposed on the input features to improve the representation ability for speaker modeling.
Meta-Causal Feature Learning for Out-of-Distribution Generalization
Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features.
What Makes for Automatic Reconstruction of Pulmonary Segments
The automatic reconstruction of pulmonary segments by ImPulSe is accurate in metrics and visually appealing.
Transport-Oriented Feature Aggregation for Speaker Embedding Learning
Pooling is needed to aggregate frame-level features into utterance-level representations for speaker modeling.
Learnable Frequency Filters for Speech Feature Extraction in Speaker Verification
There is no reason to expect that these features are optimal for all different tasks, including speaker verification (SV).
EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification
This paper proposes a domain transfer network, named EDITnet, to alleviate the language-mismatch problem on speaker embeddings without requiring speaker labels.
An Investigation on Applying Acoustic Feature Conversion to ASR of Adult and Child Speech
The performance of child speech recognition is generally less satisfactory compared to adult speech due to limited amount of training data.
Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech
Input to these classifiers are speech transcripts produced by automatic speech recognition (ASR) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Improving Text-Independent Speaker Verification with Auxiliary Speakers Using Graph
Given the embeddings from a pair of input utterances, a graph model is designed to incorporate additional information from a group of embeddings representing the so-called auxiliary speakers.
Detection of Consonant Errors in Disordered Speech Based on Consonant-vowel Segment Embedding
This paper investigates a neural network based approach to detecting consonant errors in disordered speech using consonant-vowel (CV) diphone segment in comparison to using consonant monophone segment.
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
ResNeXt, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers.
Switchable Normalization for Learning-to-Normalize Deep Representation
Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer?
SSN: Learning Sparse Switchable Normalization via SparsestMax
Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax.
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN).
Differentiable Learning-to-Normalize via Switchable Normalization
We hope SN will help ease the usage and understand the normalization techniques in deep learning.
