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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Mathew, Anjali Raniya Sanjan, Harsha Amjith, S.B. Cherian, Reni K Alex, Starlet Ben Srivastava, Priyanka Yarra, Chiranjeevi Saintgits College Of Engineering Kottayam India IIIT Cognitive Science Lab Hyderabad India Language Technologies Research Center IIIT Hyderabad India

ISBN: (纸本)9798331506032

Depression, a pervasive mental health issue, highlights the critical need for early detection and effective intervention. A database (InStant-EMDB) is developed to analyze variations in the spoken responses of individuals with depression compared to healthy counterparts. Spoken responses are ob-tained from English and Malayalam bilingual speakers who respond spontaneously to a set of 15 emotionally evocative words. These words are sourced from the Affective Norms for English Words (ANEW) dataset, which includes valence and arousal ratings for each word. The speech in both English and Malayalam is manually transcribed to accurately reflect the spoken content. The dataset also contains self-reported af-fective ratings and data from a mental health survey (PHQ-9) collected from the participants to determine their mental state, which we considered the self-reported depression labels. A preliminary analysis is conducted on the collected speech using current state-of-the-art deep learning models such as Con-volutional Neural Networks (CNN), Long Short-Term Mem-ory networks (LSTM) and Bi-directional Long Short-Term Memory networks (Bi-LSTM). Although distinct linguistic patterns exhibited by individuals struggling with depression are successfully identified by all three models in both Malay-alam and English spoken responses, the highest accuracy is achieved by the LSTM models. Our findings and dataset em-phasize the potential of linguistic patterns as valuable cues for the early identification and intervention of depression and could contribute to enhancing accessibility for diverse populations. © 2024 IEEE.

关键词： Linguistics

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IIITSaint-EmoMDB: Carefully Curated Malayalam Speech Corpus with Emotion and Self-Reported Depression Ratings 27

IIITSaint-EmoMDB: Carefully Curated Malayalam Speech Corpus ...

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27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024

作者： Thomas, Christa Vats, Guneesh Johnson, Aravind George, Ashin George, Talit Sara Cherian, Reni K Srivastava, Priyanka Yarra, Chiranjeevi Saintgits College Of Engineering CSE Kottayam India IIIT Cognitive Science lab Hyderabad India Language Technologies Research Center IIIT Hyderabad India

ISBN: (纸本)9798331506032

Mental health conditions such as depression and anxiety are pervasive issues that affect millions of individuals worldwide. Understanding the intricate relationship between emotional perception and mental health is crucial. This work develops a database named IIITSaint-EmoMDB containing Malayalam language speech samples annotated with emotion ratings at valence and arousal scale using three annotators considering four emotion labels: happy, sad, angry and neutral. In addition to emotion ratings, emotional perception is rated from 150 participants according to the circumplex model of va-lence and arousal. The database also consists of self-reported mental health scores of those participants based on PHQ-9 and GAD-7 questionnaire. Our preliminary analysis of comparing the participants' emotional perception based PHQ-9 and GAD-7 scores revealed that the depressed category showed the highest deviation from the emotion perception of people compared to the mild depression and non-depression categories. © 2024 IEEE.

关键词： depression emotion emotional perception mental health valence and arousal

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InStant-EMDB: A Multi Model Spontaneous English and Malayalam Speech Corpora for Depression Detection

InStant-EMDB: A Multi Model Spontaneous English and Malayala...

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Oriental COCOSDA International Conference on Speech Database and Assessments

作者： Anjali Mathew Raniya Harsha Sanjan Amjith S B Reni K Cherian Starlet Ben Alex Priyanka Srivastava Chiranjeevi Yarra Saintgits College Of Engineering Kottayam India Cognitive Science Lab IIIT Hyderabad India Language Technologies Research Center IIIT Hyderabad India

ISBN: (数字)9798331506032

ISBN: (纸本)9798331506049

关键词： Deep learning Surveys Analytical models Accuracy Databases Neural networks Mental health Linguistics Depression Long short term memory

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Multi-view Transformer-Based Network for Prerequisite Learning in Concept Graphs 23rd

Multi-view Transformer-Based Network for Prerequisite Learn...

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23rd International Semantic Web Conference, ISWC 2024

作者： Wang, Zhichun Shao, Yifeng Peng, Boci Li, Bangui Li, Yun Wang, Qianren Li, Nijun School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing China School of Intelligence Science and Technology Peking University Beijing China School of Computer Science and Engineering Beihang University Beijing China Advanced Cognitive AI Lab Shanghai Huawei Technologies Shanghai China

ISBN: (纸本)9783031778438

Prerequisite learning is the task of identifying prerequisite relations among concepts, which is important for many AI-based educational applications. Previous approaches explore different kinds of learning resources to obtain useful features for predicting prerequisites. Early approaches use handcrafted features while recent approaches use neural networks to encode information of concepts. To further improve the results of prerequisite learning, we build concept graphs to include comprehensive information about concepts from open data and learning resources, and propose a Multi-view Transformer-based Network (MTN) to encode multi-view features of concepts in the graph. Multi-view features are fused to make accurate predictions of concept prerequisites. We evaluate our approach on four public datasets and compare it with recently published approaches. The results show that our approach can achieve state-of-the-art results in the task of prerequisite learning. The source code and data are available at https://***/kg-bnu/MTN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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IIITSaint-EmoMDB: Carefully Curated Malayalam Speech Corpus with Emotion and Self-Reported Depression Ratings

IIITSaint-EmoMDB: Carefully Curated Malayalam Speech Corpus ...

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Oriental COCOSDA International Conference on Speech Database and Assessments

作者： Christa Thomas Guneesh Vats Aravind Johnson Ashin George Talit Sara George Reni K Cherian Priyanka Srivastava Chiranjeevi Yarra CSE Saintgits College Of Engineering Kottayam India Cognitive Science lab IIIT Hyderabad India Language Technologies Research Center IIIT Hyderabad India

ISBN: (数字)9798331506032

ISBN: (纸本)9798331506049

关键词： Databases Anxiety disorders Focusing Mental health Depression

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Language-Agnostic Analysis of Speech Depression Detection

Language-Agnostic Analysis of Speech Depression Detection

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IEEE India Conference (INDICON)

作者： Sona Binu Jismi Jose Fathima Shimna K V Alino Luke Hans Reni K. Cherian Starlet Ben Alex Priyanka Srivastava Chiranjeevi Yarra CSE Saintgits College of Engineering Kottayam India CSE Saintgits College of Engineering ECE Saintgits College of Engineering Cognitive Science Lab IIIT Hyderabad India Language Technologies Research Center IIIT Hyderabad India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

The people with Major Depressive Disorder (MDD) exhibit the symptoms of tonal variations in their speech compared to the healthy counterparts. However, these tonal variations not only confine to the state of MDD but also on the language, which has unique tonal patterns. This work analyzes automatic speech-based depression detection across two languages, English and Malayalam, which exhibits distinctive prosodic and phonemic characteristics. We propose an approach that utilizes speech data collected along with self-reported labels from participants reading sentences from IViE corpus, in both English and Malayalam. The IViE corpus consists of five sets of sentences: simple sentences, WH-questions, questions without morphosyntactic markers, inversion questions and coordinations, that can naturally prompt speakers to speak in different tonal patterns. Convolutional Neural Networks (CNNs) are employed for detecting depression from speech. The CNN model is trained to identify acoustic features associated with depression in speech, focusing on both languages. The model's performance is evaluated on the collected dataset containing recordings from both depressed and non-depressed speakers, analyzing its effectiveness in detecting depression across the two languages. Our findings and collected data could contribute to the development of language-agnostic speech-based depression detection systems, thereby enhancing accessibility for diverse populations.

关键词： Analytical models Focusing Mental health Machine learning Linguistics Speech enhancement Depression Acoustics Recording Convolutional neural networks

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Vacuum wetting of Ag/TA2 to develop a novel micron porous Ti with significant biocompatibility and antibacterial activity

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Journal of Materials Science & Technology 2022年第21期116卷 180-191页

作者： Guanpeng Liu Yulong Li Ming Yan Jicai Feng Jian Cao Min Lei Quanwen Liu Xiaowu Hu Wenqin Wang Xuewen Li Key Lab for Robot and Welding Automation of Jiangxi Province Mechanical and Electrical Engineering SchoolNanchang UniversityNanchang 330031China Jiangxi Provincial Key Laboratory of Interdisciplinary Science Nanchang UniversityNanchang 330031China Department of Materials Science and Engineering and Shenzhen Key Laboratory for Additive Manufacturing of High-Performance Materials Southern University of Science and TechnologyShenzhen 518055China State Key Lab of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China The National Engineering Research Center for Bioengineering Drugs and the Technologies Institute of Translational MedicineNanchang UniversityNanchang 330031China The Engineering Training Center of Nanchang University Mechanical and Electrical Engineering SchoolNanchang UniversityNanchang 330031China

Porous Ti with low modulus,excellent bio-corrosion resistance,biocompatibility,and antibacterial activity is highly pursued as advanced implant *** this work,a new approach to prepare micron porous structures on the surface layer of a grade 2 commercially-pure Ti(TA2)was proposed,which utilized a simple vacuum wetting process of pure Ag on the surface of *** microstructure,corrosion resistance,biocompatibility,mechanical properties,antibacterial ability,and formation mechanism of the asfabricated porous Ti were *** results show that the pores(with average pore sizes of 0.5-5μm)are distributed on the surface layer of the TA2 with a depth of~10μ*** particular,a large number of silver nanoparticles(Ag NPs)form which are dispersed on the porous *** formation mechanisms of the porous structures and Ag NPs were elucidated,suggesting that the volatilization/sublimation of Ag in TA2 is *** porous Ti possesses excellent bio-corrosion resistance,surface wettability,biocompatibility,antibacterial activity,and a relatively low elastic modulus of 40-55 GPa,which may have a promising future in the field of orthopedic *** work also provides a novel idea for the development of advanced porous Ti materials for orthopedic-related basic research and biomedical applications.

关键词： Porous titanium Biomaterials Vacuum wetting Porous surface Antibacterial activity

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Language-Agnostic Analysis of Speech Depression Detection

arXiv

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arXiv 2024年

作者： Binu, Sona Jose, Jismi Shimna, Fathima K.V. Hans, Alino Luke Cherian, Reni K. Alex, Starlet Ben Srivastava, Priyanka Yarra, Chiranjeevi CSE Saintgits College of Engineering Kottayam India CSE Saintgits College of Engineering India ECE Saintgits College of Engineering India Cognitive Science Lab IIIT Hyderabad India Language Technologies Research Center IIIT Hyderabad India

The people with Major Depressive Disorder (MDD) exhibit the symptoms of tonal variations in their speech compared to the healthy counterparts. However, these tonal variations not only confine to the state of MDD but also on the language, which has unique tonal patterns. This work analyzes automatic speech-based depression detection across two languages, English and Malayalam, which exhibits distinctive prosodic and phonemic characteristics. We propose an approach that utilizes speech data collected along with self-reported labels from participants reading sentences from IViE corpus, in both English and Malayalam. The IViE corpus consists of five sets of sentences: simple sentences, WH-questions, questions without morphosyntactic markers, inversion questions and coordinations, that can naturally prompt speakers to speak in different tonal patterns. Convolutional Neural Networks (CNNs) are employed for detecting depression from speech. The CNN model is trained to identify acoustic features associated with depression in speech, focusing on both languages. The model’s performance is evaluated on the collected dataset containing recordings from both depressed and non-depressed speakers, analyzing its effectiveness in detecting depression across the two languages. Our findings and collected data could contribute to the development of language-agnostic speech-based depression detection systems, thereby enhancing accessibility for diverse populations. © 2024, CC BY.

关键词： Convolutional neural networks

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An Untethered Soft robotic Gripper with Adjustable Grasping Modes and Force Feedback

An Untethered Soft Robotic Gripper with Adjustable Grasping ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Yang, Yang Zhu, Honghui Liu, Jia Li, Yingtian Zhou, Jianshu Ren, Tao Ren, Yi School of Automation Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Hong Kong School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu610059 China Advanced Manufacturing Lab Huawei Technologies Shenzhen China

ISBN: (纸本)9781665481090

In this paper, we propose a three-finger soft robotic gripper, which adopts an untethered and under-actuated design with simple structure and easy operation. The rotation of two servomotors is used to control the positions of the fingers, which provides the fingers with adjustable grasping modes and improves the adaptation of the gripper to objects with different shapes. A pressure sensor is integrated at the end of the finger to provide force feedback, which is used to detect the contact force when grasping the object and enables the gripper to provide the proper grasping force. The experimental section characterizes the bending of the soft robotic finger, and the gripper is integrated at the end of a collaborative robotic arm to demonstrate its adjustable grasping posture and force feedback function. The soft robotic gripper is expected to be used in a variety of scenarios, such as service robot, food industry or garbage sorting. © 2022 IEEE.

关键词： Grippers

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Learning and processing the ordinal information of temporal sequences in recurrent neural circuits 23

Learning and processing the ordinal information of temporal ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xiaolong Zou Zhikun Chu Qinghai Guo Jie Cheng Bo Hong Si Wu Yuanyuan Mi Qiyuan Lab Beijing China and School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Peking-Tsinghua Center for Life Sciences Center of Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing China and Beijing Academy of Artificial Intelligence Beijing China China and School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Peking-Tsinghua Center for Life Sciences Center of Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing China and Beijing Academy of Artificial Intelligence Beijing China Huawei Technologies Qiyuan Lab Beijing China and Biomedical Engineering School of Medicine Tsinghua University School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Peking-Tsinghua Center for Life Sciences Center of Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing China and Beijing Academy of Artificial Intelligence Beijing China Department of Psychology Tsinghua University

Temporal sequence processing is fundamental in brain cognitive functions. Experimental data has indicated that the representations of ordinal information and contents of temporal sequences are disentangled in the brain, but the neural mechanism underlying this disentanglement remains largely unclear. Here, we investigate how recurrent neural circuits learn to represent the abstract order structure of temporal sequences, and how the disentangled representation of order structure facilitates the processing of temporal sequences. We show that with an appropriate training protocol, a recurrent neural circuit can learn tree-structured attractor dynamics to encode the corresponding tree-structured orders of temporal sequences. This abstract temporal order template can then be bound with different contents, allowing for flexible and robust temporal sequence processing. Using a transfer learning task, we demonstrate that the reuse of a temporal order template facilitates the acquisition of new temporal sequences, if these sequences share the same or partial ordinal structure. Using a key-word spotting task, we demonstrate that the tree-structured attractor dynamics improves the robustness of temporal sequence discrimination, if the ordinal information is the key to differentiate these sequences. We hope that this study gives us insights into the mechanism of representing the ordinal information of temporal sequences in the brain, and helps us to develop brain-inspired temporal sequence processing algorithms.

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