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Elevating Knowledge-Enhanced Entity and Relationship Understanding for Sarcasm Detection

IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3356-3371页

作者： Wang, Xiaobao Wang, Yujing He, Dongxiao Yu, Zhe Li, Yawen Wang, Longbiao Dang, Jianwu Jin, Di Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin300350 China Beijing University of Posts and Telecommunications College of Economics and Management Beijing100876 China Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen518055 China

Sarcasm thrives on popular social media platforms such as Twitter and Reddit, where users frequently employ it to convey emotions in an ironic or satirical manner. The ability to detect sarcasm plays a pivotal role in comprehending individuals' true sentiments. To achieve a comprehensive grasp of sentence semantics, it is crucial to integrate external knowledge that can aid in deciphering entities and their intricate relationships within a sentence. Although some efforts have been made in this regard, their use of external knowledge is still relatively superficial. Specifically, Knowledge-enhanced entity and relationship understanding still face significant challenges. In this paper, we propose the Knowledge Enhanced Sentiment Dependency Graph Convolutional Network (KSDGCN) framework, which constructs a commonsense-augmented sentiment graph and a commonsense-replaced dependency graph for each text to explicitly capture the role of external knowledge for sarcasm detection. Furthermore, we validate the irrational relationships between co-occurring entity pairs within sentences and background knowledge by a signed attention mechanism. We conduct experiments on four benchmark datasets, and the results show that KSDGCN outperforms existing state-of-the-art methods and is highly interpretable. © 2025 IEEE. All rights reserved.

关键词： Knowledge graph

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A Prompt Learning Framework with Large Language Model Augmentation for Few-shot Multi-label Intent Detection

A Prompt Learning Framework with Large Language Model Augmen...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhuang, Ning Wei, Xiao Li, Junlei Wang, Xiaobao Wang, Chenyang Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Shenzhen China Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350368741

Intent detection (ID) is essential in spoken language understanding, especially in multi-label settings where intent labels are interdependent and diverse. Existing methods like SE-MLP and QA-FT struggle in few-shot settings, due to limited data availability and efficiency concerns. To address this, we introduce a Prompt Learning framework with large language Model Augmentation (PLMA) for few-shot multi-label ID. In this study, we make three contributions. First, PLMA integrates large language models (LLMs) with small language models (SLMs), using LLMs to enhance SLMs, with prompt learning as the core framework. Second, it leverages LLMs to improve the model's understanding of both queries and labels by intent spans extraction and answer space expansion. Third, PLMA refines the QA-FT. A novel QA format template is designed to allow single-step training and inference for each input, improving efficiency. Experiments on NLU++ show that PLMA significantly outperforms other baselines in both in-domain and cross-domain settings, demonstrating its effectiveness in combining the strengths of both small and large models. © 2025 IEEE.

关键词： large language model multi-label intent detection prompt learning spoken language understanding

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A Prompt Learning Framework with Large Language Model Augmentation for Few-shot Multi-label Intent Detection

A Prompt Learning Framework with Large Language Model Augmen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ning Zhuang Xiao Wei Junlei Li Xiaobao Wang Chenyang Wang Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Huiyan Technology (Tianjin) Co. Ltd Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Intent detection (ID) is essential in spoken language understanding, especially in multi-label settings where intent labels are interdependent and diverse. Existing methods like SE-MLP and QA-FT struggle in few-shot settings, due to limited data availability and efficiency concerns. To address this, we introduce a Prompt Learning framework with large language Model Augmentation (PLMA) for few-shot multi-label ID. In this study, we make three contributions. First, PLMA integrates large language models (LLMs) with small language models (SLMs), using LLMs to enhance SLMs, with prompt learning as the core framework. Second, it leverages LLMs to improve the model’s understanding of both queries and labels by intent spans extraction and answer space expansion. Third, PLMA refines the QA-FT. A novel QA format template is designed to allow single-step training and inference for each input, improving efficiency. Experiments on NLU++ show that PLMA significantly outperforms other baselines in both in-domain and cross-domain settings, demonstrating its effectiveness in combining the strengths of both small and large models.

关键词： Training Adaptation models Accuracy Large language models Signal processing Natural language processing Acoustics Multilingual Speech processing

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Discrete Unit-based Low-latency Multi-lingual Speech Synthesis for LIMMITS’25 Challenge

Discrete Unit-based Low-latency Multi-lingual Speech Synthes...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yu Jiang Cheng Gong Tianrui Wang Chunyu Qiang Haoyu Wang Qiuyu Liu Yuheng Lu Xiaobao Wang Xiaolei Zhang Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Institute of Artificial Intelligence (TeleAI) China Telecom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we present the system developed by our team, CCATTS, for the LIMMITS’25 challenge, focusing on few-shot and zero-shot TTS. We adopt a two-stage TTS strategy. In track 1, we fine-tune the pre-trained ZMM-TTS model and successfully achieve multilingual low-latency TTS. In track 2, we propose a token-based framework by modifying the first-stage model of ZMM-TTS, disentangling speech features into four types of discrete tokens—Content, Acoustic, Emotion, and Speaker—and integrating it with our designed token2wav module. This module consists of a HiFi-GAN-style decoder, an acoustic refiner, and U-Net flow matching, to generate high-quality speech. The official competition results demonstrate that our method achieves strong performance in both tracks.

关键词： Representation learning Focusing Signal processing Acoustics Multilingual Text to speech Decoding Low latency communication

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Augmenting Short Enrollment Speech via Synthesis for Target Speaker Extraction

Augmenting Short Enrollment Speech via Synthesis for Target ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Zikang Lin, Jingru Ge, Meng Jiang, Yu Wang, Xiaobao Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Shenzhen China Department of Electrical and Computer and Engineering National University of Singapore Singapore Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350368741

A high-quality enrollment speech is crucial to target speaker extraction (TSE), since it provides essential cues for identifying the target speaker in the mixture. However, real applications usually only permit a short enrollment speech, e.g. a wakeup word for a mobile device, that provides limited cues. To address this issue, we propose an enrollment augmentation strategy that allows us to enrich the limited enrollment speech with massive text data through speech synthesis. By doing so, the extended enrollment speech contains enhanced speaker timbre and phonetic content which leads to better extraction quality. Furthermore, we propose a training data augmentation strategy to improve the model's robustness and generalization in short enrollment speech scenarios. Experiments on Libri2Mix demonstrate that our proposed strategies bring a significant improvement in extreme scenarios where only 0.5s and 1-word enrollment speech is provided. We also release our code at https://***/HuangZikang-TJU/Aug4TSE. © 2025 IEEE.

关键词： continue speaking data augmentation short enrollment speech synthesis Target speaker extraction

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Augmenting Short Enrollment Speech via Synthesis for Target Speaker Extraction

Augmenting Short Enrollment Speech via Synthesis for Target ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zikang Huang Jingru Lin Meng Ge Yu Jiang Xiaobao Wang Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Department of Electrical and Computer and Engineering National University of Singapore Huiyan Technology (Tianjin) Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A high-quality enrollment speech is crucial to target speaker extraction (TSE), since it provides essential cues for identifying the target speaker in the mixture. However, real applications usually only permit a short enrollment speech, e.g. a wakeup word for a mobile device, that provides limited cues. To address this issue, we propose an enrollment augmentation strategy that allows us to enrich the limited enrollment speech with massive text data through speech synthesis. By doing so, the extended enrollment speech contains enhanced speaker timbre and phonetic content which leads to better extraction quality. Furthermore, we propose a training data augmentation strategy to improve the model’s robustness and generalization in short enrollment speech scenarios. Experiments on Libri2Mix demonstrate that our proposed strategies bring a significant improvement in extreme scenarios where only 0.5s and 1-word enrollment speech is provided. We also release our code at https://***/HuangZikang-TJU/Aug4TSE.

关键词： Training data Speech enhancement Phonetics Signal processing Speech Robustness Mobile handsets Data models Timbre Speech synthesis

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A Chinese Expressive Long-dialogue Speech Dataset with Scripts

A Chinese Expressive Long-dialogue Speech Dataset with Scrip...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jin Li Tianrui Wang Meng Ge Chenrui Cui Jiahui Zhao Jianrong Wang Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Huiyan Technology (Tianjin) Co. Ltd Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the advancement of large-scale models, the demand for emotionally rich, long-context, and highly natural communication in human-computer interaction increases. However, the exploration of long-context or script-level speech conversation tasks remains limited due to the lack of specific supervised data. To address this, we introduce a three-stage data processing pipeline for creating a Chinese expressive long-dialogue speech dataset with scripts (CELSDS). We collect videos from TV series, manually annotate speaker information for each character, apply Optical Character Recognition (OCR) to extract speech content, annotate episode summaries, and use a large language model (LLM) to generate sentence-level scenario descriptions. To our knowledge, this is the first Chinese long-context dialogue dataset that incorporates speaker and content annotations, script-level episode summaries, and sentence-level scenario details. Using this dataset, we develop a baseline model for both speech-to-script and script-to-speech generation tasks. The annotations and data production code are open-sourced at: https://***/lijin0120/CELSDS.

关键词： TV Annotations Pipelines Optical character recognition Speech recognition Production Signal processing Data processing Speech processing Videos

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Progressive Residual Extraction Based Pre-Training for Speech Representation Learning

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1825-1837页

作者： Tianrui Wang Jin Li Ziyang Ma Rui Cao Xie Chen Longbiao Wang Meng Ge Xiaobao Wang Yuguang Wang Jianwu Dang Nyima Tashi Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Huiyan Technology (Tianjin) Company Ltd. Tianjin China Saw Swee Hock School of Public Health National University of Singapore Singapore Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Information Science and Technology Tibet University Lhasa China State Key Laboratory of Tibetan Intelligence Lhasa China

Self-supervised learning (SSL) has garnered significant attention in speech processing, particularly excelling in linguistic tasks such as speech recognition. However, improving the performance of pre-trained models across various downstream tasks—each requiring distinct types of speech information—remains a significant challenge. To address this, we propose a progressive residual extraction based SSL method, named ProgRE. Specifically, we introduce two lightweight, specialized task modules into an encoder-style SSL backbone to enhance its ability to extract pitch variation and speaker information from speech. Furthermore, to mitigate the incompatibility between the reinforced pitch variation and speaker information and the learning of content information, we employ residual extraction, leveraging the extracted representations as references or conditioning signals to guide the subsequent modules in more effectively learning content-related information under the supervision of HuBERT-based speech masking prediction. In this manner, we can incrementally extract pitch variation, speaker, and content representations from the input speech. Finally, these multiple representations, each capturing diverse speech information, are combined using different layer weights to produce task-specific representations for various downstream tasks. Experimental results demonstrate that our ProgRE achieves significant performance improvements across several tasks, such as speaker identification, speech recognition, emotion recognition, speech enhancement, and voice conversion, outperforming excellent SSL methods like wav2vec2.0, HuBERT, and WavLM.

关键词： Data mining Feature extraction Transformers Speech recognition Training Speech enhancement Acoustics Vector quantization Self-supervised learning Germanium

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Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models

arXiv

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arXiv 2025年

作者： Zhang, Quanjun Fang, Chunrong Zheng, Yi Zhang, Yaxin Zhao, Yuan Huang, Rubing Zhou, Jianyi Yang, Yun Zheng, Tao Chen, Zhenyu Department of Computing Technologies Swinburne University of Technology Melbourne Australia State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Macau University of Science and Technology Jiangsu Nanjing China Huawei Cloud Computing Technologies Co. Ltd. Beijing China Shenzhen Research Institute of Nanjing University Guangdong Shenzhen China

Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been proposed to generate test assertions automatically, including deep learning (DL)-based, retrieval-based, and integration-based ones. Among them, recent integration-based approaches inherit from both DL-based and retrieval-based approaches and are considered state-of-the-art. Despite being promising, such integration-based approaches suffer from inherent limitations, such as retrieving assertions with lexical matching while ignoring meaningful code semantics, and generating assertions with a limited training corpus. In this paper, we propose a novel Retrieval-Augmented Deep Assertion Generation approach, namely RetriGen, based on a hybrid assertion retriever and a pre-trained language model (PLM)-based assertion generator. Given a focal-test, RetriGen first builds a hybrid assertion retriever to search for the most relevant test-assert pair from external codebases. The retrieval process takes both lexical similarity and semantical similarity into account via a token-based and an embedding-based retriever, respectively. RetriGen then treats assertion generation as a sequence-to-sequence task and designs a PLM-based assertion generator to predict a correct assertion with historical test-assert pairs and the retrieved external assertion. Although our concept is general and can be adapted to various off-the-shelf encoder-decoder PLMs, we implement RetriGen to facilitate assertion generation based on the recent CodeT5 model. We conduct extensive experiments to evaluate RetriGen against six state-of-the-art approaches across two large-scale datasets and two metrics. The experimental results demonstrate that RetriGen achieves 57.66% and 73.24% in terms of accuracy and CodeBLEU, outperforming all baselines with an average improvement of 50.66

关键词： Semantics

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Reducing the Gap Between Pretrained Speech Enhancement and Recognition Models Using a Real Speech-Trained Bridging Module

Reducing the Gap Between Pretrained Speech Enhancement and R...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhongjian Cui Chenrui Cui Tianrui Wang Mengnan He Hao Shi Meng Ge Caixia Gong Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin China DiDi Chuxing Beijing China Graduate School of Informatics Kyoto University Kyoto Japan Huiyan Technology (Tianjin) Co. Ltd Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The information loss or distortion caused by single-channel speech enhancement (SE) harms the performance of automatic speech recognition (ASR). Observation addition (OA) is an effective post-processing method to improve ASR performance by balancing noisy and enhanced speech. Determining the OA coefficient is crucial. However, the currently supervised OA coefficient module, called the bridging module, only utilizes simulated noisy speech for training, which has a severe mismatch with real noisy speech. In this paper, we propose training strategies to train the bridging module with real noisy speech. First, DNSMOS is selected to evaluate the perceptual quality of real noisy speech with no need for the corresponding clean label to train the bridging module. Additional constraints during training are introduced to enhance the robustness of the bridging module further. Each utterance is evaluated by the ASR back-end using various OA coefficients to obtain the word error rates (WERs). The WERs are used to construct a multidimensional vector. This vector is introduced into the bridging module with multi-task learning and is used to determine the optimal OA coefficients. The experimental results on the CHiME-4 dataset show that the proposed methods all had significant improvement compared with the simulated data trained bridging module, especially under real evaluation sets.

关键词： Training Refining Speech recognition Speech enhancement Multitasking Vectors Robustness Noise robustness Noise measurement Optimization

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