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ChildTinyTalks (CTT): A Benchmark Dataset and Baseline for Expressive Child Speech Synthesis 26th

ChildTinyTalks (CTT): A Benchmark Dataset and Baseline for E...

引用

26th International Conference on Speech and Computer

作者： Alwaisi, Shaimaa Al-Radhi, Mohammed Salah Nemeth, Geza Budapest Univ Technol & Econ Dept Telecommun & Artificial Intelligence Budapest Hungary

ISBN: (纸本)9783031779602;9783031779619

Designing expressive speech synthesis for child voice remains an unresolved problem. One of the major dilemmas faced by child TTS systems and child speech synthesis is the scarcity of datasets to train opaque data-hungry DNN-based models. Only a few datasets were proposed for the purpose of building child conversational AI agents, and many of them come with challenges such as noisy data and indiscernible speech. With this in mind, we introduce the ChildTinyTalks (CTT) dataset, comprising 2 h of speech collected from 25 kids in grades ranging from third to fourth grade, who are telling stories and sharing their experiences. The new dataset containing 1200 audio samples has been transcribed at the word level, comprising 4 classes of voice expressions. To verify the effectiveness of CTT in real-world situations, AutoVocoder models were trained and synthesized samples were generated. The models were trained on both the LJSpeech large scale dataset and our CTT dataset. Initial experimental results indicate that the CTT dataset can steadily give comparable results with acoustic model trained on a large-scale dataset with a size of less than 10% of the large dataset.

关键词： Child Speech Dataset Child Speech AutoVocoder

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Enhanced Nominal Compound Chain Extraction with Boundary and Chain Information 13th

Enhanced Nominal Compound Chain Extraction with Boundary and...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Ren, Han Peng, Minna Wan, Jing Guangdong Univ Foreign Studies Lab Language Engn & Comp Guangzhou Peoples R China Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou Peoples R China Guangdong Univ Foreign Studies Ctr Linguist & Appl Linguist Guangzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Nominal compound chain extraction (NCCE) aims to identify and cluster nominal compounds(NC) within documents. Existing methodologies suffer from the unsatisfying performance of nominal compound chain extraction due to the incorrect identification of nominal compound boundary and the clustering errors. In this paper, we propose a joint model for the NCCE task. For document representation, a multi-head attention approach is adopted to learn the contextual representation of a document for nominal compound chain extraction;for nominal compound recognition, boundary detection is employed to enhance the model's ability to recognize boundaries of nominal compounds;for chain extraction, chain detection is utilized to verify the semantic coherence of the predicted nominal compound chain. Experimental results show the efficiency of the information-enhanced approach in the NCCE task. Such method can also be applied in complex scenario NC recognition tasks, e.g., domain-specific NC recognition or loanword recognition.

关键词： Nominal Compound Chain Extraction Lexical Chain Nominal Compound Recognition Nominal Compound Clustering

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TPE: Towards Better Compositional Reasoning over Cognitive Tools via Multi-persona Collaboration 13th

TPE: Towards Better Compositional Reasoning over Cognitive T...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Hongru Wang, Huimin Wang, Lingzhi Hu, Minda Wang, Rui Xue, Boyang Huang, Yongfeng Wong, Kam-Fai Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China MoE Key Lab High Confidence Software Technol CUHK Hong Kong Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Previous works in tool learning mainly focus on the external function tools, such as models and APIs, while overlooking the existence of cognitive tools inside the cognitive/thinking processing of human mind. In this paper, we expand the scope of tools, centering on cognitive tools within the context of dialogue systems, such as multiple sources and psychological or tutoring strategies being dynamically applied in a single turn to compose helpful responses. To further enhance the reasoning and planning capability of LLMs with these cognitive tools, we introduce a multi-persona collaboration framework: Think-Plan-Execute (TPE). This framework decouples the response generation process into three distinct roles: Thinker, Planner, and Executor. Specifically, the Thinker analyzes the internal status exhibited in the dialogue context, such as user emotions and preferences, to formulate a global guideline. The Planner then generates executable plans to call different cognitive tools (e.g., sources or strategies), while the Executor compiles all intermediate results into a coherent response. Experimental results on three public datasets (FoCus, CIMA, and PsyQA) demonstrate the effectiveness and robustness of TPE, revealing the promising potential of cognitive tools as a substantial expansion of current toolset.

关键词： Dialogue System Tool Learning Large Language Model

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An Enhanced Method for Mongolian-Chinese Neural Machine Translation Using Multilingual Datastores and Chinese-Centric Methods 13th

An Enhanced Method for Mongolian-Chinese Neural Machine Tran...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Bailun Ji, Yatu Wu, Nier Liu, Xu Wang, Yanli Mao, Rui Zhou, Chao Jia, Yepai Zhao, Chen Ren, Qing-Dao-Er-Ji Liu, Na Inner Mongolia Univ Technol Hohhot 010000 Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Low-resource language translation remains a significant challenge in natural language processing, particularly for the Mongolian-Chinese language pair under the "Belt and Road" initiative. Existing translation systems struggle with this pair due to the scarcity of high-quality data. This paper addresses these challenges by combining multilingual k-nearest-neighbor machine translation (kNNMT) with Chinese-centric methods. We constructed a robust multilingual datastore and introduced an incomplete-trust loss function to effectively manage low-quality data. Additionally, we implemented re-ranking techniques to further enhance the robustness and accuracy of the translation model. The experimental results indicate that this combined approach significantly improves Mongolian-Chinese translation quality on the mBART model, with a BLEU score increase of 3.81 points and a TER score decrease of 0.0531 points. Our findings demonstrate that integrating kNN-MT with Chinese-centric methods and employing advanced loss functions and re-ranking techniques can effectively address data scarcity and quality issues, leading to substantial improvements in translation performance for low-resource language pairs.

关键词： Neural Machine Translation Chinese-Centric Multilingual Datastores Mongolian-Chinese

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Chinese Metaphor Recognition Using a Multi-stage Prompting Large Language Model 13th

Chinese Metaphor Recognition Using a Multi-stage Prompting L...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Jie Wang, Jin Zhang, Xuejie Yunnan Univ Sch Informat Sci & Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Metaphors are common in everyday language, and the identification and understanding of metaphors are facilitated by models to achieve a better understanding of the text. Metaphors are mainly identified and generated by pre-trained models in existing research, but situations, where tenors or vehicles are not included in the metaphor, cannot be handled. The problem can be effectively solved by using Large Language Models (LLMs), but significant room for exploration remains in this early-stage research area. A multi-stage generative heuristic-enhanced prompt framework is proposed in this study to enhance the ability of LLMs to recognize tenors, vehicles, and grounds in Chinese metaphors. In the first stage, a small model is trained to obtain the required confidence score for answer candidate generation. In the second stage, questions are clustered and sampled according to specific rules. Finally, the heuristic-enhanced prompt needed is formed by combining the generated answer candidates and demonstrations. The proposed model achieved the 3rd place in Track 1 of Subtask 1, the 1st place in Track 2 of Subtask 1, and the 1st place in both tracks of Subtask 2 at the NLPCC-2024 Shared Task 9.

关键词： Chinese metaphor generation Multi-stage prompting Large language model DeBERTa

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DRAK: Unlocking Molecular Insights with Domain-Specific Retrieval-Augmented Knowledge in LLMs 13th

DRAK: Unlocking Molecular Insights with Domain-Specific Retr...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Liu, Jinzhe Huang, Xiangsheng Chen, Zhuo Fang, Yin Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Xiongan Inst Innovat Hebei Key Lab Cognit Intelligence Baoding Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Large Language Models (LLMs) typically manifest knowledge gap in specialized applications due to pre-training on generalized textual corpora. Although fine-tuning and modality alignment aim to bridge this gap, their inability to provide comprehensive knowledge coverage leads to LLMs delivering imprecise responses. To address these challenges, we introduce a scalable and adaptable non-parametric knowledge injection framework, Domain-specific Retrieval-Augmented Knowledge (DRAK), aimed at bolstering LLMs' knowledge reasoning ability through context examples. DRAK integrates retrieval enhancement and structured knowledge graph recall of high-quality instances, utilizing retrieved examples to unlock LLMs' context-relevant molecular learning capabilities, offering a universal solution for specific domains. Our validation of DRAK's effectiveness and generalizability in the biomolecular domain, achieving superior performance across twelve tasks involving both molecule-oriented and bioinformatics texts within the Mol-Instructions dataset. This demonstration of DRAK's ability to unearth molecular insights establishes a standardized approach for LLMs in navigating the complexities of knowledge-intensive challenges.

关键词： Retrieval-augmented knowledge Knowledge injection Biomolecular domain

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OpenAV: Bilingual Dataset for Audio-Visual Voice Control of a Computer for Hand Disabled People 26th

OpenAV: Bilingual Dataset for Audio-Visual Voice Control of ...

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26th International Conference on Speech and Computer

作者： Ivanko, Denis Ryumin, Dmitry Axyonov, Alexandr Kashevnik, Alexey Karpov, Alexey Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg 199178 Russia

ISBN: (纸本)9783031779602;9783031779619

In recent years, audio-visual speech recognition (AVSR) assistance systems have gained increasing attention from researchers as an important part of human-computer interaction (HCI). The objective of this paper is to further advance the development of assistive technologies in the AVSR field by introducing a multi-modal OpenAV dataset, intended for state-of-the-art neural network model training. The OpenAV is designed to train AVSR models for assistance to persons without hands or with disabilities of their hands or arms in HCI. The dataset could also be useful for ordinary users at hands-free contactless HCI. The dataset currently includes the recordings in two languages (English and Russian) of 15 speakers with a minimum of 10 recording sessions for each. Along with this we provide a detailed description of the dataset and its collection pipeline. In addition, we evaluate state-of-the-art audio-visual (AV) speech recognition approach and present a baseline recognition results. We also describe the recording methodology, release the recording software to public, as well as open the access to the dataset https://***/OpenAV-dataset/.

关键词： Audio-Visual Dataset Human-Computer Interaction Automatic Speech Recognition Data Collection Machine Learning

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ASRLM: ASR-Robust Language Model Pre-training via Generative and Discriminative Learning 13th

ASRLM: ASR-Robust Language Model Pre-training via Generative...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Hu, Qian Han, Xue Wang, Yiting Wang, Yitong Deng, Chao Feng, Junlan China Mobile Res Inst JiuTian Team Beijing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

The rise of voice interface applications has renewed interest in improving the robustness of spoken language understanding(SLU). Many advances have come from end-to-end speech-language joint training, such as inferring semantics directly from speech signals and post-editing automatic speech recognition (ASR) output. Despite their performance achievements, these methods either suffer from the unavailability of a large number of paired error-prone ASR transcriptions and ground-truth annotations or are computationally costly. To mitigate these issues, we propose an ASR-robust pre-trained language model (ASRLM), which involves a generator generating simulated ASR transcriptions from ground-truth annotations and a sample-efficient discriminator distinguishing reasonable ASR errors from unrealistic ones. Experimental results demonstrate that ASRLM improves performance on a wide range of SLU tasks in the presence of ASR errors while saving 27% of the computation cost compared to baselines. Analysis also shows that our proposed generator is better than other simulation methods, including both BERT and GPT4-based, at simulating real-world ASR error situations.

关键词： Spoken language understanding Automatic speech recognition Pre-trained language model

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Ancient Egyptian Hieroglyphic Texts Structure Identification 26th

Ancient Egyptian Hieroglyphic Texts Structure Identification

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26th International Conference on Speech and Computer

作者： Marik, Radek Landgrafova, Renata Liska, Jiri Czech Tech Univ Fac Elect Engn Dept Telecommun Engn Prague Czech Republic Charles Univ Prague Fac Arts Czech Inst Egyptol Prague Czech Republic Czech Tech Univ Fac Elect Engn Dept Comp Sci Prague Czech Republic

ISBN: (纸本)9783031780134;9783031780141

In our project, we deal with translating texts recorded by the ancient Egyptian civilization into contemporary English. In this paper, we focus particularly on identification of text structures consisting of hieroglyphs. The identification is based on classification of segmented image blobs and their spatial relations using graph neural networks. We reached 99.8% accuracy on a dataset of facsimiles created for the shaft tomb of Menekhibnekau. The high accuracy is due to a combination of precise results achieved by the CRAFT method, additional features like the size of the hieroglyphs, including very robust topological properties of blob adjacency weighted by the learned nonlinear graph neural network scheme not relying on simple horizontal or vertical projections as used in standard OCR approaches. The graph of blob spatial relations is built using distance transform. We also propose an algorithm for a separation of hieroglyphs from mostly linear structures delineating the strips of hieroglyphs if they touch. Strips of hieroglyphs identified this way can be used to extract blobs of glyphs into reading sequences before their classification to Gardiner's codes, transliteration and translation.

关键词： Ancient Egyptian hieroglyph Text structure detection Graph neural network

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DFS-QA: Dynamic Frame Selection for Better Video Question Answering 13th

DFS-QA: Dynamic Frame Selection for Better Video Question An...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Ren, Zhibo Hou, Baoyu Wang, Huizhen Zhu, Muhua Xiao, Tong Zhu, Jingbo Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China NiuTrans Res Shenyang Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Previous approaches to the task of video question answering (VideoQA) rely on either frame-level representations or region-level representations to encode visual contents of a video. Regarding the former stream of approaches, one strategy is to utilize all the frames selected according to a fixed time interval and the other strategy is to select a single frame to stand for the whole video. In this paper, we repel these two extremes and focus on the question of whether dynamically selecting frames can achieve better performance. To this end, we propose a novel weakly-supervised approach to dynamic frame selection. In addition, to make better use of selected frames, we utilize a multi-instance learning method to learn to predict answers based on frame-question pairs rather than the holistic representation of all the selected frames. Extensive experiments on benchmarks show that the proposed approach achieves the best or competitive performance, which is very promising especially when we take its relative simplicity into consideration.

关键词： Video Question Answering Multimodal Learning

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