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Weakly-Supervised Depression Detection in speech Through Self-Learning Based Label Correction

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IEEE Transactions on Audio, Speech and Language Processing

IEEE Transactions on Audio, speech and language processing 2025年 33卷 748-758页

作者： Yanfei Sun Yuanyuan Zhou Xinzhou Xu Jin Qi Feiyi Xu Zhao Ren Björn W. Schuller School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Wuxi University Wuxi China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria Key Laboratory of Modern Acoustics MOE Nanjing University Nanjing China Cognitive Systems Lab University of Bremen Bremen Germany Chair of Health Informatics Technische Universität München (TUM) München Germany Munich Data Science Institute Munich Germany Munich Center for Machine Learning Munich Germany GLAM – the Group on Language Audio & Music Imperial College London London U.K.

Automated Depression Detection (ADD) in speech aims to automatically estimate one's depressive attributes through artificial intelligence tools towards spoken signals. Nevertheless, existing speech-based ADD works fail to sufficiently consider weakly-supervised cases with inaccurate labels, which may typically appear in intelligent mental health. In this regard, we propose the Self-Learning-based Label Correction (SLLC) approach for weakly-supervised depression detection in speech. The proposed approach employs a self-learning manner connecting a label correction module and a depression detection module. Within the approach, the label correction module fuses likelihood-ratio-based and prototype-based label correction strategies in order to effectively correct the inaccurate labels, while the depression detection module aims at detecting depressed samples through a 1D convolutional recurrent neural network with multiple types of losses. The experimental results on two depression detection corpora show that our proposed SLLC approach performs better compared with existing state-of-the-art speech-based depression detection approaches, in the case of weak supervision with inaccurate labels for depression detection in speech.

关键词： Depression Feature extraction Training speech processing Prototypes Noise measurement Telecommunications Sun Internet of Things Weak supervision

LocalViT: Analyzing Locality in Vision Transformers

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LocalViT: Analyzing Locality in Vision Transformers

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yawei Li Kai Zhang Jiezhang Cao Radu Timofte Michele Magno Luca Benini Luc Van Goo Computer Vision Lab D-ITET ETH Zurich Switzerland Center for Artificial Intelligence and Data Science (CAIDAS) University of Wurzburg Germany Center for Project-Based Learning D-ITET ETH Zurich Switzerland Integrated Systems Laboratory D-ITET ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Processing Speech and Images (PSI) KU Leuven Belgium

The aim of this paper is to study the influence of locality mechanisms in vision transformers. Transformers originated from machine translation and are particularly good at modelling long-range dependencies within a long sequence. Although the global interaction between the token embeddings could be well modelled by the self-attention mechanism of transformers, what is lacking is a locality mechanism for infor-mation exchange within a local region. In this paper, locality mechanism is systematically investigated by carefully designed controlled experiments. We add locality to vision transformers into the feed-forward network. This seemingly simple solution is inspired by the comparison between feed-forward networks and inverted residual blocks. The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to vision transformers with different architecture designs, which shows the generalization of the locality concept. For ImageNet2012 classification, the locality-enhanced transformers outperform the baselines Swin-T [1], DeiT-T [2] and PVT-T [3] by 1.0%, 2.6 % and 3.1 % with a negligible increase in the number of parameters and computational effort. Code is available at https://***/ofsoundof/LocalViT.

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SUMMARY ON THE ICASSP 2022 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION GRAND CHALLENGE

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arXiv 2022年

作者： Yu, Fan Zhang, Shiliang Guo, Pengcheng Fu, Yihui Du, Zhihao Zheng, Siqi Huang, Weilong Xie, Lei Tan, Zheng-Hua Wang, DeLiang Qian, Yanmin Lee, Kong Aik Yan, Zhijie Ma, Bin Xu, Xin Bu, Hui Speech Lab Alibaba Group China Speech Lab Alibaba Group Singapore AISHELL Foundation China Beijing Shell Shell Technology Co. Ltd. Beijing China Department of Electronic Systems Aalborg University Aalborg Denmark Department of Computer Science and Engineering Center for Cognitive and Brain Sciences The Ohio State University United States SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University China Aural and Language Intelligence Department Institute for Infocomm Research A*STAR Singapore

The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Challenge (M2MeT) focuses on one of the most valuable and the most challenging scenarios of speech technologies. The M2MeT challenge has particularly set up two tracks, speaker diarization (track 1) and multi-speaker automatic speech recognition (ASR) (track 2). Along with the challenge, we released 120 hours of real-recorded Mandarin meeting speech data with manual annotation, including far-field data collected by 8-channel microphone array as well as near-field data collected by each participants’ headset microphone. We briefly describe the released dataset, track setups, baselines and summarize the challenge results and major techniques used in the submissions. © 2022, CC BY-SA.

关键词： speech recognition

Robust Deep Learning Framework for Predicting Respiratory Anomalies and Diseases 42

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Robust Deep Learning Framework for Predicting Respiratory An...

42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Pham, Lam McLoughlin, Ian Phan, Huy Tran, Minh Nguyen, Truc Palaniappan, Ramaswamy University of Kent School of Computing Medway Kent United Kingdom University of Science and Technology of China National Engineering Laboratory for Speech and Language Information Processing Hefei China Queen Mary University School of Electronic Engineering and Computer Science London United Kingdom University of Oxford Nuffield Department of Anaesthesia United Kingdom Graz University of Technology Signal Processing and Speech Communication Lab Austria

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119908

This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this paper. Firstly, we provide an extensive analysis of how factors such as respiratory cycle length, time resolution, and network architecture, affect final prediction accuracy. Secondly, a novel deep learning based framework is proposed for detection of respiratory diseases and shown to perform extremely well compared to state of the art methods. © 2020 IEEE.

关键词： Network architecture

Overview of the Tenth Dialog System Technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio speech and language processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

Gap detection responses modelled using the Hill equation in adults with well-controlled HIV

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International Audiology 2023年第5期62卷 383-392页

作者： Christopher E. Niemczak Christopher Cox Gevorg Grigoryan Gayle Springer Abigail M. Fellows Peter Torre III Howard J. Hoffman Jay C. Buckey Michael W. Plankey a Department of Biomedical Research Geisel School of Medicine at Dartmouth Hanover NH USA b Department of Epidemiology Johns Hopkins University Bloomberg School of Public Health Baltimore MD USA c Department of Biological and Computer Science Dartmouth College Hanover NH USA d School of Speech Language and Hearing Sciences San Diego State University San Diego CA USA e National Institute on Deafness and Other Communication Disorders Bethesda MD USA f Department of Medicine Division of General Internal Medicine Georgetown University Medical Center Washington DC USA

ObjectiveThis study’s objective was determining whether gap detection deficits are present in a longstanding cohort of people living with HIV (PLWH) compared to those living without HIV (PLWOH) using a new gap detection modelling technique (i.e. fitting gap responses using the Hill equation and analysing the individual gap detection resulting curves with non-linear statistics). This approach provides a measure of both gap threshold and the steepness of the gap length/correct detection *** relationship between the correct identification rate at each gap length was modelled using the Hill equation. Results were analysed using a nonlinear mixed-effect regression *** sample45 PLWH (age range 41–78) and 39 PLWOH (age range 38–79) were enrolled and completed gap detection *** likelihood ratio statistic comparing the full regression model with the HIV effects to the null model, assuming one population curve for both groups, was highly significant (p< 0.001), suggesting a less precise relationship between gap length and correct detection in *** showed degraded gap detection ability compared to PLWOH, likely due to central nervous system effects of HIV infection or treatment. The Hill equation provided a new approach for modelling gap detection ability.

关键词： Gap detection HIV Hill equation non-linear modeling central auditory processing

Attack on practical speaker verification system using universal adversarial perturbations

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arXiv 2021年

作者： Zhang, Weiyi Zhao, Shuning Liu, Le Li, Jianmin Cheng, Xingliang Zheng, Thomas Fang Hu, Xiaolin Department of Computer Science and Technology BNRist Tsinghua University China Center for Speech and Language Technologies BNRist Tsinghua University China Beijing d-Ear Technologies Co. Ltd.

In authentication scenarios, applications of practical speaker verification systems usually require a person to read a dynamic authentication text. Previous studies played an audio adversarial example as a digital signal to perform physical attacks, which would be easily rejected by audio replay detection modules. This work shows that by playing our crafted adversarial perturbation as a separate source when the adversary is speaking, the practical speaker verification system will misjudge the adversary as a target speaker. A two-step algorithm is proposed to optimize the universal adversarial perturbation to be text-independent and has little effect on the authentication text recognition. We also estimated room impulse response (RIR) in the algorithm which allowed the perturbation to be effective after being played over the air. In the physical experiment, we achieved targeted attacks with success rate of 100%, while the word error rate (WER) on speech recognition was only increased by 3.55%. And recorded audios could pass replay detection for the live person speaking. Copyright © 2021, The Authors. All rights reserved.

关键词： speech recognition

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Deep generative LDA

arXiv 2020年

作者： Cai, Yunqi Wang, Dong Department of Computer Science and Technology Center for Speech and Language Technologies Tsinghua University Beijing China

Linear discriminant analysis (LDA) is a popular tool for classification and dimension reduction. Limited by its linear form and the underlying Gaussian assumption, however, LDA is not applicable in situations where the data distribution is complex. Recently, we proposed a discriminative normalization flow (DNF) model. In this study, we reinterpret DNF as a deep generative LDA model, and study its properties in representing complex data. We conducted a simulation experiment and a speaker recognition experiment. The results show that DNF and its subspace version are much more powerful than the conventional LDA in modeling complex data and retrieving low-dimensional representations. Copyright © 2020, The Authors. All rights reserved.

关键词： Discriminant analysis

Analyzing political parody in social media

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arXiv 2020年

作者： Maronikolakis, Antonis Villegas, Danae Sánchez Preoţiuc-Pietro, Daniel Aletras, Nikolaos Center for Information and Language Processing Lmu Munich Computer Science Department University of Sheffield Bloomberg

Parody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in so-cial media through many popular parody ac-counts. In this paper, we present the first com-putational study of parody. We introduce a new publicly available data set of tweets from real politicians and their corresponding parody ac-counts. We run a battery of supervised machine learning models for automatically de-tecting parody tweets with an emphasis on ro-bustness by testing on tweets from accounts unseen in training, across different genders and across countries. Our results show that po-litical parody tweets can be predicted with an accuracy up to 90%. Finally, we identify the markers of parody through a linguistic analy-sis. Beyond research in linguistics and politi-cal communication, accurately and automati-cally detecting parody is important to improv-ing fact checking for journalists and analytics such as sentiment analysis through filtering out parodical utterances. Copyright © 2020, The Authors. All rights reserved.

关键词： Sentiment analysis