检索结果-内蒙古大学图书馆

2025 IEEE International Conference on Acoustics, speech, and Signal processing, ICASSP 2025

作者： Cai, Pengfei Song, Yan Jiang, Nan Gu, Qing McLoughlin, Ian National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China China ICT Cluster Singapore Institute of Technology Singapore

ISBN: (纸本)9798350368741

A significant challenge in sound event detection (SED) is the effective utilization of unlabeled data, given the limited availability of labeled data due to high annotation costs. Semi-supervised algorithms rely on labeled data to learn from unlabeled data, and the performance is constrained by the quality and size of the former. In this paper, we introduce the Prototype based Masked Audio Model (PMAM) algorithm for self-supervised representation learning in SED, to better exploit unlabeled data. Specifically, semantically rich frame-level pseudo labels are constructed from a Gaussian mixture model (GMM) based prototypical distribution modeling. These pseudo labels supervise the leaning of a Transformer-based masked audio model, in which binary cross-entropy loss is employed instead of the widely used InfoNCE loss, to provide independent loss contributions from different prototypes, which is important in real scenarios in which multiple labels may apply to unsupervised data frames. A final stage of fine-tuning with just a small amount of labeled data yields a very high performing SED model. On like-for-like tests using the DESED task, our method achieves a PSDS1 score of 62.5%, surpassing current state-of-the-art models and demonstrating the superiority of the proposed technique. © 2025 IEEE.

关键词： masked audio model prototype self-supervised learning sound event detection

来源：评论

学校读者我要写书评

暂无评论

English to Arabic Braille Neural Machine Translation Through Corpus Augmentation 6

English to Arabic Braille Neural Machine Translation Through...

引用

6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Joshi, Nisheeth Katyayan, Pragya Ahmed, Syed Afroz Speech and Language Processing Lab Center for Artificial Intelligence Vidyapith Rajasthan Banasthali India School of Computer Science University of Petroleum and Energy Studies Utttrakhand India Umm Al Quwain University United Arab Emirates

In this paper we have shown the development of English to Arabic Braille Neural Machine Translation (NMT) System. For our experiments we have developed two NMT systems. The first was the baseline NMT system which was trained only on the English-Arabic parallel corpus whereas the second NMT system has some sub modules for handling syntax transfer of English to Arabic and translation/transliteration of English Named Entities into Arabic equivalents. Then the system used this code-mixed text to train the NMT model. The English-Arabic parallel corpus was taken from Opus parallel corpus repository. For preprocessing of the English text Stanford's Stanza NLP Library was used. The trained NMT models were evaluated using standard automatic MT evaluation metrics and their results were also corelated with human judgements. It was found that the corpus augmented NMT system performed better than the baseline NMT system. © 2024 The Authors. Published by Elsevier B.V.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

IndirectQA: Understanding Indirect Answers to Implicit Polar Questions in French and Spanish 30

IndirectQA: Understanding Indirect Answers to Implicit Polar...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Müller, Christin Plank, Barbara MaiNLP Lab Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9782493814104

Polar questions are common in dialogue and expect exactly one of two answers (yes/no). It is however not uncommon for speakers to bypass these expected choices and answer, for example, "Islands are generally by the sea" to the question: "An island? By the sea?". While such answers are natural in spoken dialogues, conversational systems still struggle to interpret them. Seminal work to interpret indirect answers were made in recent years-but only for English and with strict question formulations. In this work, we present a new corpus for French and Spanish-IndirectQA -where we mine subtitle data for indirect answers to study the labeling task with six different labels, while broadening polar questions to include also implicit polar questions (statements that trigger a yes/no-answer which are not necessarily formulated as a question). We opted for subtitles since they are a readily available source of conversation in various languages, but also come with peculiarities and challenges which we will discuss. Overall, we provide the first results on French and Spanish. They show that the task is challenging: the baseline accuracy scores drop from 61.43 on English to 44.06 for French and Spanish. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Annotation Corpora Indirect Answers Natural language Understanding

来源：评论

学校读者我要写书评

暂无评论

Slot and Intent Detection Resources for Bavarian and Lithuanian: Assessing Translations vs Natural Queries to Digital Assistants 30

Slot and Intent Detection Resources for Bavarian and Lithuan...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Winkler, Miriam Juozapaityte, Virginija van der Goot, Rob Plank, Barbara MaiNLP Lab Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9782493814104

Digital assistants perform well in high-resource languages like English, where tasks like slot and intent detection (SID) are well-supported. Many recent SID datasets start including multiple language varieties. However, it is unclear how realistic these translated datasets are. Therefore, we extend one such dataset, namely xSID-0.4 (van der Goot et al., 2021a;Aepli et al., 2023), to include two underrepresented languages: Bavarian, a German dialect, and Lithuanian, a Baltic language. Both language variants have limited speaker populations and are often not included in multilingual projects. In addition to translations we provide "natural" queries to digital assistants generated by native speakers. We further include utterances from another dataset for Bavarian to build the richest SID dataset available today for a low-resource dialect without standard orthography. We then set out to evaluate models trained on English in a zero-shot scenario on our target language variants. Our evaluation reveals that translated data can produce overly optimistic scores. However, the error patterns in translated and natural datasets are highly similar. Cross-dataset experiments demonstrate that data collection methods influence performance, with scores lower than those achieved with single-dataset translations. This work contributes to enhancing SID datasets for underrepresented languages, yielding NaLiBaSID, a new evaluation dataset for Bavarian and Lithuanian. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Translation (languages)

来源：评论

学校读者我要写书评

暂无评论

Stargan-vc Based Cross-Domain Data Augmentation for Speaker Verification 48

Stargan-vc Based Cross-Domain Data Augmentation for Speaker ...

引用

48th IEEE International Conference on Acoustics, speech and Signal processing, ICASSP 2023

作者： Hu, Hang-Rui Song, Yan Zhang, Jian-Tao Dai, Li-Rong McLoughlin, Ian Zhuo, Zhu Zhou, Yu Li, Yu-Hong Xue, Hui University of Science and Technology of China National Engineering Research Center for Speech and Language Information Processing Hefei China Alibaba Group China

ISBN: (纸本)9781728163277

Automatic speaker verification (ASV) faces domain shift caused by the mismatch of intrinsic and extrinsic factors, such as recording device and speaking style, in real-world applications, which leads to severe performance degradation. Since single-speaker multi-condition (SSMC) data is difficult to collect in practice, existing domain adaptation methods are hard to ensure the feature consistency of the same class but different domains. To this end, we propose a cross-domain data generation method to obtain a domain-invariant ASV system. Inspired by voice conversion (VC) task, a StarGAN based generative model first learns cross-domain mappings from SSMC data, and then generates missing domain data for all speakers, thus increasing the intra-class diversity of the training set. Considering the difference between ASV and VC task, we renovate the corresponding training objectives and network structure to make the adaptation task-specific. Evaluations on achieve a relative performance improvement of about 5-8% over the baseline in terms of minDCF and EER, outperforming the CNSRC winner's system of the equivalent scale. © 2023 IEEE.

关键词： Data Augmentation Domain Adaptation Speaker Verification StarGAN

来源：评论

学校读者我要写书评

暂无评论

Known Words Will Do: Unknown Concept Translation via Lexical Relations 5

Known Words Will Do: Unknown Concept Translation via Lexical...

引用

5th Workshop on Technologies for Machine Translation of Low-Resource languages, LoResMT 2022 at 29th International Conference on Computational Linguistics. COLING 2022

作者： Wu, Winston Yarowsky, David Computer Science and Engineering University of Michigan United States Center for Language and Speech Processing Johns Hopkins University United States

Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of specialized terms into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach. © 2022 COLING. All Rights Reserved.

关键词： Translation (languages)

来源：评论

学校读者我要写书评

暂无评论

The USTC System for EEG-Music Emotion Recognition Challenge

The USTC System for EEG-Music Emotion Recognition Challenge

引用

2025 IEEE International Conference on Acoustics, speech, and Signal processing, ICASSP 2025

作者： Chen, Jiaxin Wang, Yiming Liu, Yin-Long Feng, Rui Yuan, Jiahong Ling, Zhen-Hua National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China Interdisciplinary Research Center for Linguistic Sciences University of Science and Technology of China Hefei China

ISBN: (纸本)9798350368741

This paper presents the Neural Harmony team's submission to Task 1 (Person Identification) of the ICASSP 2025 EEG-Music Emotion Recognition Challenge, which aims to identify the subject from a given EEG segment. To enhance performance, we propose a novel architecture incorporating the Multiscale ConvBlock and integrating attention mechanisms with convolutional networks. We also reprocessed the data and trained multiple models with different train-validation splits, which were ensembled during testing to further improve robustness. Our final results on the test data exceed the challenge baseline, achieving 100% accuracy in Person Identification. Additionally, unseen subjects were introduced to evaluate the model's generalization ability, and the results confirm the model's strong adaptability to new subjects. © 2025 IEEE.

关键词： attention convolutional network EEG Person Identification

来源：评论

学校读者我要写书评

暂无评论

KARRIEREWEGE: A Large Scale Career Path Prediction Dataset 31

KARRIEREWEGE: A Large Scale Career Path Prediction Dataset

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Senger, Elena Campbell, Yuri van der Goot, Rob Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Fraunhofer Center for International Management and Knowledge Economy IMW Germany Department of Computer Science IT University of Copenhagen Denmark

ISBN: (纸本)9798891761971

Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce KARRIEREWEGE, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in KARRIEREWEGE+. This allows for accurate predictions from unstructured data, closely aligning with real-world application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a prior benchmark and observe improved performance and robustness, particularly for free-text use cases, due to the synthesized data. ©2025 Association for Computational Linguistics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CN-CVS: A Mandarin Audio-Visual Dataset for Large Vocabulary Continuous Visual to speech Synthesis 48

CN-CVS: A Mandarin Audio-Visual Dataset for Large Vocabulary...

引用

48th IEEE International Conference on Acoustics, speech and Signal processing, ICASSP 2023

作者： Chen, Chen Wang, Dong Zheng, Thomas Fang Tsinghua University Center for Speech and Language Technologies BNRist China Tsinghua University Department of Computer Science and Technology China

ISBN: (纸本)9781728163277

Research on Video to speech Synthesis (VTS) surges recently and the focus is gradually shifting from small-vocabulary short-phrase VTS to large-vocabulary continuous VTS (LVC-VTS). A large-scale dataset with sufficient speakers and utterances is a prerequisite for such research, and the database is certainly language *** this paper, we introduce CN-CVS, a large-scale Mandarin continuous visual-speech dataset, to support LVC-VTS research. The dataset contains about 200k utterances from more than 2500 individuals, amounting to more than 300 hours of visual-speech data. We built a state-of-the-art VTS model with the new dataset and conducted preliminary studies. Our results show that models that achieve good performance on small vocabulary tasks may perform very poor on CN-CVS, indicating that continuous VTS is indeed a challenging task, and the main challenge comes from the unconstrained vocabulary. The dataset and baseline code can be downloaded for free from http://***. © 2023 IEEE.

关键词： audio-visual dataset Mandarin video to speech synthesis

来源：评论

学校读者我要写书评

暂无评论

TEAR: A Cross-Modal Pre-Trained Text Encoder Enhanced by Acoustic Representations for speech Synthesis

IEEE Transactions on Audio, Speech and Language Processing

引用

IEEE Transactions on Audio, speech and language processing 2025年 33卷 1117-1128页

作者： Shiming Wang Yang Ai Liping Chen Yajun Hu Zhenhua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China

Text encoders play an important role in text-to-speech (TTS) by analyzing text input and converting it into linguistic representations. In order to generate expressive speech from text, pre-training text encoders on large amounts of data has recently become a solution to generate richer and more effective linguistic representations. However, existing pre-trained text encoders only use the self-supervised target on the text data, without considering the relationship between text and speech modalities during the pre-training stage. In this paper, we propose TEAR, a cross-modal pre-trained Text Encoder enhanced by Acoustic Representations for TTS. In addition to conventional text pre-training, TEAR incorporates speech pre-training to extract semantic and prosody-related acoustic representations from speech. Then, TEAR introduces a novel cross-modal pre-training task for the text encoder, termed acoustics-aware joint prediction. This task leverages the acoustic representations generated by the preceding speech pre-training, enabling the linguistic representation to perceive and comprehend prosody during the encoding process. In our implementation, TEAR was pre-trained on 130 million unlabeled Chinese and English sentences, as well as 740,000 Chinese text-speech pairs. The results of the downstream TTS experiments on three expressive TTS datasets indicate that the proposed TEAR can encode more effective and comprehensive linguistic representations compared to the text-only pre-trained encoders, leading to the generation of more natural speech.

关键词： Acoustics Linguistics Encoding Bidirectional control Training Data models Predictive models speech enhancement Transformers Context modeling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：