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检索条件"丛书名=Lecture notes in artificial intelligence,"

共 57438 条记录，以下是171-180 订阅

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Topic-Awared Contrastive Learning for Incoming Fake News Detection in News Streams 13th

Topic-Awared Contrastive Learning for Incoming Fake News Det...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhang, Yongcheng Xiang, Changpeng Ren, Kai Wei, Xiaomei Huazhong Agr Univ Coll Informat Wuhan Peoples R China South Cent Minzu Univ Coll Comp Sci Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Agr Wuhan Peoples R China

ISBN: (纸本)9789819794423;9789819794430

The prevalence of social media streams has transformed them into essential sources of real-time information on various topics. However, the simultaneous spread of fake news poses a significant challenge to the authenticity of information dissemination. Traditional fake news detection methods, which focus on analyzing individual articles in news streams, often neglect the topic-based correlations crucial for identifying misinformation patterns. This oversight hinders their ability to effectively detect fake news, especially when it relates to similar topics with a tendency for falsehoods. To mitigate this limitation, we propose the Topic-Awared Contrastive Learning Framework (TCLF), an efficient mechanism for analyzing topic-based correlations. The TCLF comprises two key components: the dynamic correlation mapping module, which employs incremental clustering to categorize news articles into familiar and unfamiliar topic domains, and the contrastive correlation learning module, which applies contrastive learning techniques to identify fake news across diverse topics. Our framework better detects fake news in news streams, outperforming existing state-of-the-art methods.

关键词： Fake News Detection Incremental Clustering Contrastive Learning

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Exploring the Potential of Prompting Methods in Low-Resource Speech Recognition with Whisper 13th

Exploring the Potential of Prompting Methods in Low-Resource...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Chen, Yaqi Zhang, Wenlin Zhang, Hao Yang, Xukui Qu, Dan Informat Engn Univ Sch Informat Syst Engn Zhengzhou Peoples R China Lab Adv Comp & Intelligence Engn Wuxi Jiangsu Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Recent advancements in large-scale pre-trained automatic speech recognition (ASR) foundation models (e.g., Whisper) have exhibited remarkable performance in speech processing tasks. But fine-tuning such models for low-resource languages can be computationally expensive and prone to overfitting. Prompting methods offer a solution by designing specific prompts in the inputs that guide the model's behavior for targeted tasks, facilitating parameter-efficient adaptation. This paper presents the first exploration of various prompt tuning methods and optimized strategies for low-resource ASR based on Whisper. Moreover, we propose a shallow integration method to utilize the advantage of deep prompt tuning and reparametrization. Extensive experiments on the Common Voice and FLEURS datasets show the competitive performance of prompt tuning compared to full fine-tuning and Lora with fewer trainable parameters. Notably, the shallow integration strategy yields impressive results, especially for small models.

关键词： low-resource automatic speech recognition Whisper prompt tuning

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Multidimensional Rhythm: Comparing Rhythmic Properties of Australian and New Zealand Monologues 26th

Multidimensional Rhythm: Comparing Rhythmic Properties of Au...

引用

26th International Conference on Speech and Computer

作者： Borzykh, Anna Shevchenko, Tatiana Moscow State Linguist Univ 38 Ostozhenka St Moscow 119034 Russia

ISBN: (纸本)9783031779602;9783031779619

The study is concerned with comparison of rhythmic features in monologues of Australian (AusE) and New Zealand (NZE) speakers. Methodology is designed to capture the advantages of both the traditional approach and the 'new paradigm' metrics to account for the two national varieties of English. The traditional for Russian linguistics description of rhythm as a hierarchy of linguistic units (ip, foot, syllable) provided data on common rhythm structure with dialect-specific differences in syllable durations, which suggested the Australian speech habits of prolonging unstressed syllables. By applying the metrics collected in Correlatore [15], we found that unmonitored monologues' rhythm could be categorized differently from reading reported in previous research. AusE speech demonstrated features of 'controlling' syllable-based rhythm, in contrast with the 'compensating' accent-based rhythm of NZE speakers. The metrics proved to be dialect-, style- and tempo-sensitive, and suitable for comparison of varieties of the same language.

关键词： Australian English New Zealand English Rhythm Metrics Accent-Based Rhythm Syllable-Based Rhythm

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Binary and Multiclass Classification of Dysphonia Using Whisper Encoder and One-Dimensional Convolutional Neural Network 26th

Binary and Multiclass Classification of Dysphonia Using Whis...

引用

26th International Conference on Speech and Computer

作者： Aziz, Dosti Sztaho, David Budapest Univ Technol & Econ Dept Telecommun & Artificial Intelligence Magyar Tudosok Korutja 2 H-1117 Budapest Hungary Univ Sulaimani Comp Sci Dept Qlyasan St Sulaymaniyah 46001 Iraq

ISBN: (纸本)9783031779602;9783031779619

Dysphonia is a condition characterized by difficulties in voice production caused by functional, psychological, and neurological factors. Accurate diagnosis of dysphonia is crucial for determining the proper treatment procedures and follow-ups. The previous approaches primarily focused on sustained vowels and manual feature extraction, which does not represent everyday speech usage. It raised questions regarding the usability of these methodologies when applied in real-life scenarios. Also, differentiating between organic and functional dysphonia remains an unexplored area in speech pathology. In this paper, we propose an approach based on Web-scale Supervised Pretraining for Speech Recognition (Whisper). Features extracted from the pre-trained transformer-based Whisper encoder in both Base and Large variations were used to train machine learning models such as Support Vector Machine, Random Forest, and Multi-Layer Perceptron. We also proposed an architecture based on a 1-dimensional convolution neural network (1DCNN). Our proposed method showed high performance and surpassed previous approaches on the same dataset in binary and multiclass classification. It achieved 95.51% accuracy in binary classification and 76.40% in multiclass classification, outperforming previous methods. These results emphasize the effectiveness of our model in capturing speech characteristics at the utterance level and distinguishing between dysphonia subtypes.

关键词： Dysphonia Whisper Speech embeddings Speech disorder

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Voice Cloning and Mismatch Conditions in Forensic Automatic Speaker Recognition 26th

Voice Cloning and Mismatch Conditions in Forensic Automatic ...

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26th International Conference on Speech and Computer

作者： Kudera, Jacek Coccia, Miriam Fadaeijouybari, Sharifeh Preidt, Till Ranjan, Akshay Braun, Angelika Trier Univ Trier Germany

ISBN: (纸本)9783031780134;9783031780141

This paper examines the influence of mismatch conditions on performance of a state-of-the-art system for forensic automatic speaker recognition (FASR) with known ground truth. The tested variables included generative AI output that is voice cloning, wearing a face mask, and increased physical effort. We hypothesized that these factors decrease the likelihood ratios when compared to baseline recordings. The outcomes of the comparison confirmed our assumptions for all hypotheses tested. The results showed a decrease in the likelihood ratios for testing the recordings with mismatch conditions when compared to the samples controlled for intrinsic and extrinsic mismatch. The findings of this study may appear relevant to automatic and semi-automatic speaker comparison and contribute to discussions on the application of automatic speaker identification methods to recordings with mismatch conditions.

关键词： Voice cloning Mismatch conditions Forensic Automatic Speaker Recognition

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PAPER: A Persona-Aware Chain-of-Thought Learning Framework for Personalized Dialogue Response Generation 13th

PAPER: A Persona-Aware Chain-of-Thought Learning Framework f...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Yameng Feng, Shi Wang, Daling Zhang, Yifei Yang, Xiaocui Northeastern Univ Dept Comp Sci Shenyang Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Endowing chatbots with a consistent persona is particularly important to generate engaging dialogues. Existing models struggle to effectively perceive and comprehend persona, especially when confronted with persona tags that have unique and obscure meanings, presenting barriers to generating a consistent response. Besides, previous studies have not fully utilized the rich persona information implied in the dialogue history. In this paper, we propose a Persona-Aware chain-of-thought (CoT) learning framework for PErsonalized Response generation (PAPER), encompassing the persona understanding stage, the persona perception stage and the response generation stage. Specifically, we first leverage persona explanation data to train the model to interpret persona tags, equipping the model with ability to comprehend and interpret personas. Moreover, the model obtained during the persona understanding stage is also employed to extract persona information from the dialogue history and generate persona explanations to enhance persona traits. Subsequently, we utilize expanded persona information and dialogue history to generate consistent response. The generated persona explanations and the persona information extracted from the dialogue are regarded as intermediate persona CoT rationales. The response generation can leverage generated persona CoT rationales that are based on persona tags and dialogue history. Extensive experiments demonstrate that PAPER outperforms baselines and can effectively improve the quality and persona consistency of generated responses.

关键词： Personalized Dialogue Systems Persona Perception Large Language Models Chain of Thought

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W2CL: A Multi-task Learning Approach to Improve Domain-Specific Sentence Classification Through Word Classification and Contrastive Learning 13th

W2CL: A Multi-task Learning Approach to Improve Domain-Speci...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Yan, Sirui Luo, Zhiyi Luo, Shuyun Qiu, Ying Zhejiang Sci Tech Univ Hangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Sentence classification task plays a crucial role in various NLP tasks. Recent studies have shown that contrastive learning can enhance the representational capability of Pre-trained Language Models (PLMs) and that different methods for constructing positive and negative samples can be applied to various downstream application scenarios. Therefore, in this study, we propose W2CL, a novel multi-task learning framework based on Word Classification and Contrastive Learning, aimed at integrating domain knowledge extracted by ChatGPT from raw corporas into PLMs and improving the performance of PLMs in domain-specific sentence classification tasks. Contrastive learning assists the model in gradually learning the semantic similarity and contextual relevance between words during the training process to enhance its ability to understand text. Word classification provides additional contextual understanding, thereby improving the model's ability to differentiate between different classes within the specific domain. Experiments demonstrate that our multi-task approach significantly outperforms other methods, leading to substantial improvements in domain-specific sentence classification performance. This framework offers a robust solution for adapting general-purpose language models to specialized domains, ensuring better performance and generalization in various domain applications.

关键词： Sentence Classification Contrastive Learning Multi-task Learning

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Transformation of Emotional Speech to Anger Speech to Reduce Mismatches in Testing and Enrollment Speech for Speaker Recognition System 26th

Transformation of Emotional Speech to Anger Speech to Reduce...

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26th International Conference on Speech and Computer

作者： Tomar, Shalini Koolagudi, Shashidhar G. Natl Inst Technol Dept Comp Sci & Engn Surathkal Karnataka India

ISBN: (纸本)9783031780134;9783031780141

Speaker Recognition (SR) is a critical component of digital speech processing. The robustness of Speaker Recognition systems is compromised by the variance in speakers' emotional states. According to a study on SR utilizing emotive speech, it seems complicated to distinguish between emotions like "anger," "sad," "fear," and "happy". Developing a speaker recognition model that works effectively using emotional speech is challenging, specifically in the case of some intense emotions like anger. This work explores emotional speech transformation approaches to reduce the mismatch between training and testing emotional speech for the SR tasks. The recommended effort aims to develop speech transformation techniques to transform different emotional speech into anger. This study modifies the prosodic features "TPIB" (Tempo, Pitch, Intensity, and Brightness) to transform the speech from neutral, happy, fearful, and sad emotions to anger. Performance evaluations of the SR system employing transformed emotional speech are obtained through integrating Mel-Spectrogram feature extraction and deep learning techniques, including the CREMA-D and NITK-KLESC datasets. The experiment results demonstrate that the suggested emotional speech transformation technique increases SR accuracy in transforming neutral by approximately 15%, happy by 11%, sad by 32%, and fear by 30%.

关键词： Speaker recognition Emotional speech transformation Tempo Pitch Intensity Timbre features Brightness

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Cluster-Based Effective Generation of AI-Driven Literature Surveys 13th

Cluster-Based Effective Generation of AI-Driven Literature S...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Zongyue Lu, Xiaofei Chen, Ling Wang, Haishan Wang, Xu Shi, Qinghui Xue, Dejun Bi, Yanhong Huang, Zixuan Tongfang Knowledge Network Technol Co Ltd CNKI Large Model & Future Technol R&D Dept Beijing Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Large language models (LLMs) have revolutionized natural language processing (NLP) tasks, yet they struggle with handling long text inputs due to token limitations. This paper introduces a novel method for creating AI-driven literature surveys by integrating clustering techniques with a modified TF-IDF formula (c-TF-IDF). Our approach organizes documents into clusters, enabling the production of comprehensive and concise literature surveys. This method not only overcomes token limitations but also improves the relevance and coverage of the generated surveys. Experimental results indicate that our approach significantly outperforms traditional LLM methods in both coverage and informativeness.

关键词： Clustering analysis Retrieval-augmented generation AI-driven scientific research

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Structure and Behavior Dual-Graph Reasoning with Integrated Key-Clue Parsing for Multi-party Dialogue Reading Comprehension 13th

Structure and Behavior Dual-Graph Reasoning with Integrated ...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Cao, Rui Zhou, Xiabing Zhou, Guodong Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Multi-party Dialogue Reading Comprehension is a reading comprehension task that involves comprehending dialogue with multiple interlocutors and answering questions. Research on MDRC faces enormous challenges because of the multiple parties involved and the frequent changes in the chat topic. Previous work has explored in mining and modeling dialogue context features on the basis of pre-trained models and graph-based models. However, two issues exist: insufficient connection between the behavioral events of dialogue participants and excessive information irrelevant to the question during reasoning. In this paper, we propose a dual-graph reasoning with integrated key-clue parsing approach. We utilize the dual-graph reasoning strategy to capture the global structure and internal dynamics of the dialogue. Moreover, we design the key-clue parsing module to prioritize essential dialogue content, which significantly reduces the burden on the model and enhances the accuracy of our model. The experiments on the benchmark dataset show that our approach yields stable and substantial improvements, and outperforms the state-of-the-art methods.

关键词： Multi-party Dialogue Reading Comprehension Structure-aware Graph Behavior-aware Graph Key-Clue Parsing

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