ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第3页

CyFi-TTS: Cyclic Normalizing Flow with Fine-Grained Representation for End-to-End Text-to-Speech CyFi-TTS:端到端文本到语音的细粒度表示循环归一化流

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10095323

In-Sun Hwang, Young-Sub Han, Byoung-Ki Jeon

引用次数: 0

Daily Mental Health Monitoring from Speech: A Real-World Japanese Dataset and Multitask Learning Analysis 每日心理健康监测:一个真实世界的日语数据集和多任务学习分析

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10096884

Meishu Song, Andreas Triantafyllopoulos, Zijiang Yang, Hiroki Takeuchi, Toru Nakamura, A. Kishi, Tetsuro Ishizawa, K. Yoshiuchi, Xin Jing, Vincent Karas, Zhonghao Zhao, Kun Qian, B. Hu, B. Schuller, Yoshiharu Yamamoto

{"title":"Daily Mental Health Monitoring from Speech: A Real-World Japanese Dataset and Multitask Learning Analysis","authors":"Meishu Song, Andreas Triantafyllopoulos, Zijiang Yang, Hiroki Takeuchi, Toru Nakamura, A. Kishi, Tetsuro Ishizawa, K. Yoshiuchi, Xin Jing, Vincent Karas, Zhonghao Zhao, Kun Qian, B. Hu, B. Schuller, Yoshiharu Yamamoto","doi":"10.1109/ICASSP49357.2023.10096884","DOIUrl":"https://doi.org/10.1109/ICASSP49357.2023.10096884","url":null,"abstract":"Translating mental health recognition from clinical research into real-world application requires extensive data, yet existing emotion datasets are impoverished in terms of daily mental health monitoring, especially when aiming for self-reported anxiety and depression recognition. We introduce the Japanese Daily Speech Dataset (JDSD), a large in-the-wild daily speech emotion dataset consisting of 20,827 speech samples from 342 speakers and 54 hours of total duration. The data is annotated on the Depression and Anxiety Mood Scale (DAMS) – 9 self-reported emotions to evaluate mood state including \"vigorous\", \"gloomy\", \"concerned\", \"happy\", \"unpleasant\", \"anxious\", \"cheerful\", \"depressed\", and \"worried\". Our dataset possesses emotional states, activity, and time diversity, making it useful for training models to track daily emotional states for healthcare purposes. We partition our corpus and provide a multi-task benchmark across nine emotions, demonstrating that mental health states can be predicted reliably from self-reports with a Concordance Correlation Coefficient value of .547 on average. We hope that JDSD will become a valuable resource to further the development of daily emotional healthcare tracking.","PeriodicalId":113072,"journal":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123448177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Cross-Domain Object Classification Via Successive Subspace Alignment 基于连续子空间对齐的跨域目标分类

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10096792

Kecheng Chen, Hao Li, Hong Yan

引用次数: 0

Priv-Aug-Shap-ECGResNet: Privacy Preserving Shapley-Value Attributed Augmented Resnet for Practical Single-Lead Electrocardiogram Classification privaug - shap - ecgresnet:用于实用单导联心电图分类的隐私保护Shapley-Value属性增强Resnet

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10096437

A. Ukil, Leandro Marín, A. Jara

{"title":"Priv-Aug-Shap-ECGResNet: Privacy Preserving Shapley-Value Attributed Augmented Resnet for Practical Single-Lead Electrocardiogram Classification","authors":"A. Ukil, Leandro Marín, A. Jara","doi":"10.1109/ICASSP49357.2023.10096437","DOIUrl":"https://doi.org/10.1109/ICASSP49357.2023.10096437","url":null,"abstract":"We aim to build an effective automated single-lead Electrocardiogram (ECG) classification system to enable remote and timely screening of critical cardio-vascular diseases like Heart attack. However, the expenses associated with cardiologist-intervened ECG annotation limits the number of training instances. While conventional deep learning models require large set of training examples for accurate classification, we propose Priv-Aug-Shap-ECGResNet which demonstrates that deep learning algorithm (for e.g., residual network or ResNet) with ablation of unimportant features from the given training dataset can ensure consistently better classification performance over relevant state-of-the-art algorithms. Additively perturbed training augmentation with Shapley attribution finds out the right feature subset with the assistance of the axioms of transferable utility, namely \"efficiency\" and \"null player\" on which Shapley value game is defined. Priv-Aug-Shap-ECGResNet is enabled with novel data privacy preservation feature through differential privacy technique to provide measured obfuscation to render ZeroR classification equivalent knowledge gain to the adversary.","PeriodicalId":113072,"journal":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125623795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Selecting Language Models Features VIA Software-Hardware Co-Design 通过软硬件协同设计选择语言模型特征

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10097191

Vlad Pandelea, E. Ragusa, P. Gastaldo, E. Cambria

引用次数: 0

Restoration of Time-Varying Graph Signals using Deep Algorithm Unrolling 基于深度展开算法的时变图信号恢复

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10094838

Hayate Kojima, Hikari Noguchi, Koki Yamada, Yuichi Tanaka

引用次数: 1

Spatially Informed Independent vector analysis for Source Extraction based on the convolutive Transfer Function Model 基于卷积传递函数模型的空间信息独立矢量分析

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10097106

Xianrui Wang, Andreas Brendel, Gongping Huang, Yichen Yang, Walter Kellermann, Jingdong Chen

引用次数: 2

An Implicit Gradient Method for Constrained Bilevel Problems Using Barrier Approximation 用势垒逼近求解约束双层问题的隐式梯度方法

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10096878

Ioannis C. Tsaknakis, Prashant Khanduri, Min-Sun Hong

引用次数: 0

M-CTRL: A Continual Representation Learning Framework with Slowly Improving Past Pre-Trained Model M-CTRL:一个具有缓慢改进过去预训练模型的持续表示学习框架

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10096793

Jin-Seong Choi, Jaehwan Lee, Chae-Won Lee, Joon‐Hyuk Chang

{"title":"M-CTRL: A Continual Representation Learning Framework with Slowly Improving Past Pre-Trained Model","authors":"Jin-Seong Choi, Jaehwan Lee, Chae-Won Lee, Joon‐Hyuk Chang","doi":"10.1109/ICASSP49357.2023.10096793","DOIUrl":"https://doi.org/10.1109/ICASSP49357.2023.10096793","url":null,"abstract":"Representation models pre-trained on unlabeled data show competitive performance in speech recognition, even when fine-tuned on small amounts of labeled data. The continual representation learning (CTRL) framework combines pre-training and continual learning methods to obtain powerful representation. CTRL relies on two neural networks, online and offline models, where the fixed latter model transfers information to the former model with continual learning loss. In this paper, we present momentum continual representation learning (M-CTRL), a framework that slowly updates the offline model with an exponential moving average of the online model. Our framework aims to capture information from the offline model improved on past and new domains. To evaluate our framework, we continually pre-train wav2vec 2.0 with M-CTRL in the following order: Librispeech, Wall Street Journal, and TED-LIUM V3. Our experiments demonstrate that M-CTRL improves the performance in the new domain and reduces information loss in the past domain compared to CTRL.","PeriodicalId":113072,"journal":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115323574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

D-CONFORMER: Deformable Sparse Transformer Augmented Convolution for Voxel-Based 3D Object Detection D-CONFORMER:基于体素的三维物体检测的可变形稀疏变压器增强卷积

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2023-06-04 DOI: 10.1109/ICASSP49357.2023.10097060

Xiao Zhao, Liuzhen Su, Xukun Zhang, Dingkang Yang, Mingyang Sun, Shunli Wang, Peng Zhai, Lihua Zhang

{"title":"D-CONFORMER: Deformable Sparse Transformer Augmented Convolution for Voxel-Based 3D Object Detection","authors":"Xiao Zhao, Liuzhen Su, Xukun Zhang, Dingkang Yang, Mingyang Sun, Shunli Wang, Peng Zhai, Lihua Zhang","doi":"10.1109/ICASSP49357.2023.10097060","DOIUrl":"https://doi.org/10.1109/ICASSP49357.2023.10097060","url":null,"abstract":"Although CNN-based and Transformer-based detectors have made impressive improvements in 3D object detection, these two network paradigms suffer from the interference of insufficient receptive field and local detail weakening, which significantly limits the feature extraction performance of the backbone. In this paper, we propose to fuse convolution and transformer, and simultaneously considering the different contributions of non-empty voxels at different positions in 3D space to object detection, it is not consistent with applying standard convolution and transformer directly on voxels. Specifically, we design a novel deformable sparse transformer to perform long-range information interaction on fine-grained local detail semantics aggregated by focal sparse convolution, termed D-Conformer. D-Conformer learns valuable voxels with position-wise in sparse space and can be applied to most voxel-based detectors as a backbone. Extensive experiments demonstrate that our method achieves satisfactory detection results and outperforms state-of-the-art 3D detection methods by a large margin.","PeriodicalId":113072,"journal":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115350873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0