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

共 57438 条记录，以下是1-10 订阅

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Separating Party Conversation by Applying Contrastive Learning Methodology 26th

Separating Party Conversation by Applying Contrastive Learni...

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26th International Conference on Speech and Computer

作者： Singaravelan, Anandakumar Hsu, Jia-Lien Fu Jen Catholic Univ Grad Inst Appl Sci & Engn Taipei Taiwan Fu Jen Catholic Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9783031780134;9783031780141

Deep learning innovations have been pivotal in advancing the field of source separation, especially in challenging environments such as distinguishing parallel conversations at a party and utilizing virtual assistants or making a call in crowded circumstance. This study introduces a novel contrastive learning-based methodology for separating two speech sources from background music in party-like situations. Given inputs that include both music and speech sources, our method applies an encoder-separator-decoder framework to be a background system for separating audio by leveraging contrastive learning. Our research adopts a multi-negative approach, employing tuplet loss and N-pair loss functions, to focus on individual audio sources. Additionally, our method utilized a specially curated dataset for party scenarios, composed of Royalty free audio from Kaggle, the Librimix dataset, and the Musan dataset. The outcomes demonstrate remarkable results, achieving a Scale-Invariant Signal-to-Noise Ratio improvements (SI-SNRi) of 14.81 dB for separating speaker 1, speaker 2 and music from the mixture, a SI-SNRi of 18.33 dB for separating both speakers from the mixture, and a SI-SNRi of 7.78 dB for separating music alone from the mixture, evidencing the effectiveness of our approach in disentangling individual audio components from complex acoustic mixtures.

关键词： Source separation Contrastive learning Single channel audio Multi-negative approach

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MQuA: Multi-level Query-Video Augmentation for Multilingual Video Corpus Retrieval 13th

MQuA: Multi-level Query-Video Augmentation for Multilingual ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Yu, Guyang Bi, Xiaoyang Tang, Jielong Gu, Ming Chen, Tianbai Li, Zhiqiang Zhu, Miankuan China Elect Technol Grp Corp Res Inst 32 Shanghai Peoples R China Xiamen Univ Sch Aerosp Engn Xiamen Peoples R China Sun Yat Sen Univ Sch Artificial Intelligence Guangzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Multilingual Video Corpus Retrieval (mVCR) aims to localize the most relevant videos in a large collection of untrimmed bilingual instructional videos to a given natural language query. Existing methods often fall into the trap of learning spurious relationships as shortcuts. This retrieval bias mainly stems from two aspects: (1) Only a single query statement for each training sample. (2) The noise from irrelevant video features. To address these issues, we propose aMulti-Level QUery-Video Augmentation (MQuA) to facilitate the cross-modal interactions between query and video. Specifically, MQuA consists of Multilingual Query Paraphrase Generation (MQPG) and Multi-Level Video Moment Refinement (MVMR). MQPG prompts large language models (LLMs) to generate diverse paraphrases of queries while preserving their primary semantics, thereby guiding the retrieval model to align the core semantics of queries with video moments. MVMR uncovers the fine-grained video moments features that are highly related to the query. This module further helps the model to learn genuine relevance while eliminating spurious correlations due to noise. Extensive experimental results show that the proposed method achieves state-of-the-art (SOTA) performance compared to other competing methods, and we rank first in the NLPCC-2024-Shared-Task-2 mVCR competition.

关键词： Multilingual Video Corpus Retrieval Multimodal information enhancement LLM

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Sparse Mixture of Experts Language Models Excel in Knowledge Distillation 13th

Sparse Mixture of Experts Language Models Excel in Knowledge...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Xu, Haiyang Liu, Haoxiang Gong, Wei Wang, Hai Deng, Xianjun Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Alibaba Grp Hangzhou Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Knowledge distillation is an effective method for reducing the computational overhead of large language models. However, recent optimization efforts in distilling large language models have primarily focused on loss functions and training methodologies, with limited attention given to structural improvements of student models. This is largely due to the challenges posed by cross-architecture distillation and the substantial computational resources required for modifying model structures. To address these issues, we introduce a novel method that integrates a sparse mixture of experts (MoE) architecture with low-rank adaptation (LoRA). This combination not only bolsters the capabilities of the student model but also facilitates knowledge distillation using MoE without the necessity of continued pretraining. Experimental results indicate that our approach enhances the model's capabilities compared to dense model distillation, achieving superior performance across a multitude of tasks. We will release our code at https://***/sprogxhy/***.

关键词： Mixture of experts Knowledge distillation Language models

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FriendRec: A Graph Neural Network for Friend Recommendation 13th

FriendRec: A Graph Neural Network for Friend Recommendation

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Bai, Yun Lai, Zanyou Guangdong Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9789819794393;9789819794409

The friend recommendation service is instrumental in molding and enhancing the expansion of online social networks. Graph Neural Networks (GNNs) have become a powerful tool for learning graph data and have been widely used in the design of recommender systems, as they can effectively aggregate the neighborhood representation of nodes. The basic idea of GNNs-based recommender systems is to utilize GNNs to aggregate a user's neighborhood information in the user-user graph and the user-item graph. For friend recommendation, combining users' neighborhood information in the user-user graph and the user-item graph makes recommendations more diverse. However, one of the challenges is how to effectively combine the neighborhood information of users from the two graphs. In addition, since users usually have many items to interact with, accurately analyzing the user's personal preferences is a challenging task. In this paper, we present a GNNs-based framework (FriendRec) for friend recommendations. We first utilize GNNs to aggregate social representation and interest representation, respectively. Then we fuse two kinds of representations with a different strategy from previous works. In particular, we design a special layer to aggregate interest representation, which achieves 5% uplift compared with state-of-the-art recommendation model. Compared to fusing representations with deep neural networks, FriendRec significantly reduces the number of parameters, making it easier to train.

关键词： Social Networks Friend Recommendation Graph Neural Networks

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Emotion Recognition by Vocalizations of Nonhuman Primates: Human and Automatic Classification 26th

Emotion Recognition by Vocalizations of Nonhuman Primates: H...

引用

26th International Conference on Speech and Computer

作者： Froloya, Olga Matveev, Anton Lyakso, Elena Kuznetsova, Tamara Golubeva, Inna St Petersburg State Univ Child Speech Res Grp St Petersburg Russia Russian Acad Sci Pavlov Inst Physiol St Petersburg Russia

ISBN: (纸本)9783031780134;9783031780141

The presented study aimed to revealing the possibility of human recognition and automatic classification of the emotional states by vocalizations of rhesus macaques. This work is a continuation of the study of emotional states manifestation in vocalizations of primates. The participants of the study were 9 unrelated rhesus macaques, aged 14 - 36 months. 10 adult people (5 specialists working with macaques and 5 listeners - physiologists with professional experience in the field of child speech) took part in the perceptual study, classified emotional states by vocalizations of macaques. The vocalizations of animals were collected in laboratory condition and annotated to emotional states: "joy-neutral state-sadness-anger-fear". The original dataset of emotional vocalizations of rhesus macaques was created. Perceptual analysis revealed that specialists could recognize all emotional states by vocalizations of macaques, with maximal accuracy for sadness, minimal accuracy - for joy state;listeners recognized emotional states worse vs specialists. In automatic classification, weobtained the Unweighted-Average Recall (UAR) value close to UAR value for recognition by specialists. Fear, joy, and sadness states were automatically classified better than anger and neutral states. The data obtained in the study of rhesus macaques' vocalizations could be useful for comparison with data on human and nonhuman primates. In practice, the information about features of affective calls of macaques is important for specialists working with animals in laboratory conditions.

关键词： Emotions Rhesus Macaques Perceptual Experiment Automatic Classification

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A Multilevel Interaction Network Framework for Multimodal Entity Linking 13th

A Multilevel Interaction Network Framework for Multimodal En...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Jia, Xiaoyu Nuo, Minghua Wang, Yao Zhang, Yuan Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Inner Mongolia Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot 010021 Inner Mongolia Peoples R China Inner Mongolia Key Lab Multilingual Artificial In Hohhot 010021 Inner Mongolia Peoples R China

ISBN: (纸本)9789819794362;9789819794379

The task of Multimodal Entity Linking (MEL) aims to link ambiguous mentions in multimodal contexts to entities in a Multimodal Knowledge Graph. However, simple inter-modal interactions may result in deficiencies when processing multimodal data. To address this, we propose a novel Multilevel Interaction Network Framework (MINF) for MEL, comprehensively exploring both intra-modal and inter-modal interactions and integration. To capture fine-grained cues within individual modalities, we designed the Text to Text Interaction Unit (TTTU) and the Image to Image Interaction Unit (ITIU). For semantic correlations between different modalities, we introduced the Text to Image Fusion Interaction Unit (TIFU) and the Text to Image Cross Interaction Unit (TICU), enhancing multimodal feature interactions between entities and mentions. We also introduce independent loss functions for specific units to improve multimodal learning while preventing over-reliance on any single unit. Experimental results on three public benchmark datasets demonstrate that our proposed framework outperforms several state-of-the-art baseline methods, and ablation studies verify the effectiveness of designed modules.

关键词： Multimodal Entity Linking Multilevel Interaction Knowledge Graph

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Knowledge-Enhanced Utterance Domain Classification with Keywords-Assisted Concept Denoising Network 13th

Knowledge-Enhanced Utterance Domain Classification with Keyw...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Huang, Peijie Huang, Boxi Xu, Yuhong Chen, Weiting Li, Jia South China Agr Univ Coll Math & Informat Guangzhou Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Utterance Domain Classification (UDC) is essential for Spoken Language Understanding (SLU), a task analogous to short text classification. Short texts are often challenging to understand due to their lack of context, necessitating the enrichment of their semantic representation with supplementary information such as concepts from external knowledge bases. However, the inclusion of concepts introduces noise, making the selection of valuable concepts challenging. This paper proposes a UDC method employing keyword-guided signals to enhance the purity of external knowledge. We use two keyword extraction strategies to construct two types of keywords. A keyword-assisted concept denoising module addresses the concept noise problem, and a knowledge injection module is designed to better integrate concepts into the model. Experimental results on two Chinese SLU datasets demonstrate that our model achieves state-of-the-art performance.

关键词： Utterance domain classification Spoken language understanding Knowledge-enhanced methods

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Practical Evaluation and Validation of Methods for Automatic Speaker Identification (as Applied to Various Languages) 26th

Practical Evaluation and Validation of Methods for Automatic...

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26th International Conference on Speech and Computer

作者： Potapova, Rodmonga Potapov, Vsevolod Kuryanova, Irina Moscow State Linguist Univ Inst Appl & Math Linguist 38 Ostozhenka St Moscow 119034 Russia Lomonosov Moscow State Univ Ctr New Technol Humanities Leninskije Gory 1 Moscow 119991 Russia Moscow State Linguist Univ Inst Appl & Math Linguist Expt Phonet Lab Criminalist Speech Translat 38 Ostozhenka St Moscow 119034 Russia Moscow Res Ctr Dept Forens Linguist 5 Nizhnyaya Syromyatnicheskaya St Moscow 105120 Russia

ISBN: (纸本)9783031780134;9783031780141

The importance of using the results of phonoscopic examination in criminal proceedings is increasingly increasing: a voice, like a fingerprint, is individual;therefore the recognition of two voices as identical is indisputable evidence. Identification of a set of speaker's identification characteristics, differentiation of group and individual characteristics with due account for sociolinguistic parameters, assessment of the stability of these characteristics and the degree of their expression requires the use of a complex of linguistic and acoustic methods for studying voice and speech. With this in mind, audio recordings of negotiations between ethnic criminal groups in languages, the phonological systems of which differ significantly from Russian, involve a certain difficulty, since the speech feature matrix traditionally used presents limitations for conducting identification studies in a language other than Russian. The problem of forensic research of foreign speech in a language unfamiliar to the expert was considered by us in previous papers devoted to the forensic identification of foreign-language speakers involving the methods of structural and melodic analysis of phonograms [1] and analysis of formant trajectories of the speech signal [2]. This paper is concerned with the consideration of fully automatic methods for identifying speakers as applied to various languages.

关键词： Phonoscopic Examination Identification Based on a Mixture of Gaussian Distributions Spectral-Formant Analysis Method for Comparing Fundamental Frequency Statistics

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GraphLLM: A General Framework for Multi-hop Question Answering over Knowledge Graphs Using Large Language Models 13th

GraphLLM: A General Framework for Multi-hop Question Answeri...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Qi, Zijian Li, Nan Huang, Chenxi Wang, Gangliang Liang, Shenglin Lin, Hui Guo, Qinglang Xiamen Univ Sch Informat Xiamen Peoples R China CETC Acad Elect & Informat Technol Grp Co Ltd Beijing Peoples R China Xidian Univ Xian Peoples R China

ISBN: (纸本)9789819794300;9789819794317

The task of multi-hop question answering over knowledge graphs (KGQA) is designed to identify answer entities for a given question through reasoning across multiple edges over KGs. This task presents persistent challenges: as the number of hops increases, both the reasoning complexity and the pool of candidate answers expand, resulting in sub-optimal outcomes. Due to the powerful semantic understanding and logical reasoning capabilities of large language models, we propose a general framework for multi-hop KQGA using large language models (LLMs), named GraphLLM. Specifically, GraphLLM involves employing the semantic understanding and reasoning abilities of LLMs to decompose multi-hop questions through a Divide-And-Conquer approach and construct sub-graphs, transforming complex problems into several simple sub-questions. We obtain the ultimate answer by iteratively using Graph Neural Networks (GNNs) to solve sub-questions. By conducting experiments on benchmarks WebQSP and MetaQA, results indicate that GraphLLM exhibits outstanding performance compared to leading methods. We successfully demonstrate a collaborative example of LLMs and GNNs, offering a novel approach to addressing intricate multi-hop KGQA.

关键词： Large Language Model Knowledge Graph Multi-hop Question Answering Graph Neural Network

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Depression Classification Using Token Merging-Based Speech Spectrotemporal Transformer 26th

Depression Classification Using Token Merging-Based Speech S...

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26th International Conference on Speech and Computer

作者： Kumar, Lokesh Kaustubh, Kumar Prasanna, S. R. Mahadeva Indian Inst Technol Dharwad Dharwad Karnataka India

ISBN: (纸本)9783031779602;9783031779619

This paper introduces a novel approach for depression classification, utilizing multimodal token merging (ToMe) within a speech spectrotemporal transformer framework. The model's efficacy is evaluated with log-mel spectrograms and autocorrelation tempograms extracted from depressed and non depressed speech. The results demonstrate the effectiveness of ToMe when integrated with attention mechanisms of audio spectrogram transformer (AST) models, such as AST and data efficient image transformer (DeiT) encoders. This underscores the importance of the token pruning mechanism utilized in the study. Additionally, a multimodal dual-channel architecture is introduced, featuring two distinct feature modalities extracted from speech: spectrograms and autocorrelation tempograms. The novel ToMe dual-channel AST and ToMe dual-channel AST with DeiT encoder models demonstrate remarkable performance on two different datasets, namely the EATD-Corpus (Chinese) and DAIC-WoZ (English), providing promising results for depression detection.

关键词： Log-mel spectrogram Autocorrelation tempogram Vision transformer Token merging (ToMe)

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