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6th EAI International Conference on science and Technologies for Smart Cities, SmartCity 2020

作者： Zapoglou, Nikolaos Patsakos, Ioannis Drosatos, George Rantos, Konstantinos Department of Computer Science International Hellenic University Kavala Greece Institute for Language and Speech Processing Athena Research Center Xanthi Greece

ISBN: (纸本)9783030760625

Internet of Things (IoT) is a promising, relatively new technology that develops "smart" networks with a variety of uses and applications (e.g., smart cities, smart home and autonomous cars). The diversity of protocols, technologies and devices that IoT consists of, even though they add in value and utility, they create major privacy issues that can be exploited by malicious entities to benefit from or even violate privacy of IoT users. The special features of blockchain technology, such as immutability, transparency, accessibility, autonomy and decentralisation, has led the academics and the industry to search for further uses of it, besides financial applications (e.g., Bitcoin) that was initially applied. This paper is a survey on the existing literature regarding blockchain-based privacy-preserving solutions that have been proposed specifically for the IoT to address personal data protection and preserve user privacy. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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An Exploratory Approach to the Corpus Filtering Shared Task WMT20 5

An Exploratory Approach to the Corpus Filtering Shared Task ...

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5th Conference on Machine Translation, WMT 2020

作者： Kejriwal, Ankur Koehn, Philipp Department of Computer Science Johns Hopkins University United States Center for Language and Speech Processing Johns Hopkins University United States

ISBN: (纸本)9781948087810

This document describes an exploratory look into the Parallel Corpus Filtering Shared Task in WMT20. We submitted scores for both Pashto-English and Khmer-English systems combining multiple techniques like monolingual language model scores, length based filters, language ID filters with confidence and norm of embedings. © 2020 Association for Computational Linguistics

关键词： Computational linguistics

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Enhancing Lip Reading with Multi-Scale Video and Multi-Encoder

Enhancing Lip Reading with Multi-Scale Video and Multi-Encod...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： He Wang Pengcheng Guo Xucheng Wan Huan Zhou Lei Xie Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xian China IT Innovation and Research Center Huawei Technologies

ISBN: (数字)9798350379815

ISBN: (纸本)9798350379822

Automatic lip-reading (ALR) aims to automatically tran-scribe spoken content from a speaker's silent lip motion captured in video. Current mainstream lip-reading approaches only use a single visual encoder to model input videos of a single scale. In this paper, we propose to enhance lip-reading by incorporating multi-scale video data and multi-encoder. Specifically, we first introduce a novel multi-scale lip motion extraction algorithm based on the size of the speaker's face and propose an Enhanced ResNet3D visual front-end (VFE) to extract lip features at different scales. For the multi-encoder, in addition to the mainstream Transformer and Conformer, we also incorporate the recently proposed Branch-former and E-Branchformer as visual encoders. In the experiments, we explore the influence of different video data scales and encoders on ALR system performance and fuse the texts transcribed by all ALR systems using recognizer output voting error reduction (ROVER). Finally, our proposed approach placed second in the ICME 2024 ChatCLR Challenge Task 2, with a 21.52% reduction in character error rate (CER) compared to the official baseline on the evaluation set.

关键词： Visualization Error analysis Text recognition Lips System performance Perturbation methods speech recognition

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EDSep: An Effective Diffusion-Based Method for speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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CAMEL: Cross-Attention Enhanced Mixture-of-Experts and language Bias for Code-Switching speech Recognition

CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Langu...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： He Wang Xucheng Wan Naijun Zheng Kai Liu Huan Zhou Guojian Li Lei Xie Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xi’an China IT Innovation and Research Center Huawei Technologies Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Code-switching automatic speech recognition (ASR) aims to transcribe speech that contains two or more languages accurately. To better capture language-specific speech representations and address language confusion in code-switching ASR, the mixture-of-experts (MoE) architecture and an additional language diarization (LD) decoder are commonly employed. However, most researches remain stagnant in simple operations like weighted summation or concatenation to fuse language-specific speech representations, leaving significant opportunities to explore the enhancement of integrating language bias information. In this paper, we introduce CAMEL, a cross-attention-based MoE and language bias approach for code-switching ASR. Specifically, after each MoE layer, we fuse language-specific speech representations with cross-attention, leveraging its strong contextual modeling abilities. Additionally, we design a source attention-based mechanism to incorporate the language information from the LD decoder output into text embeddings. Experimental results demonstrate that our approach achieves state-of-the-art performance on the SEAME, ASRU200, and ASRU700+Librispeech460 Mandarin-English code-switching ASR datasets.

关键词： speech coding Fuses speech enhancement Signal processing Logic gates Acoustics Decoding Multilingual Context modeling Automatic speech recognition

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Joint Lemmatization and Morphological Tagging with LEMMING

arXiv

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arXiv 2024年

作者： Müller, Thomas Cotterell, Ryan Fraser, Alexander Schütze, Hinrich Center for Information and Language Processing University of Munich Germany Department of Computer Science Johns Hopkins University United States

We present LEMMING, a modular log-linear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or analyzers. LEMMING sets the new state of the art in token-based statistical lemmatization on six languages;e.g., for Czech lemmatization, we reduce the error by 60%, from 4.05 to 1.58. We also give empirical evidence that jointly modeling morphological tags and lemmata is mutually beneficial. © 2024, CC BY.

关键词： Regression analysis

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MaiBaam: A Multi-Dialectal Bavarian Universal Dependency Treebank

arXiv

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arXiv 2024年

作者： Blaschke, Verena Kovačić, Barbara Peng, Siyao Schütze, Hinrich Plank, Barbara Center for Information and Language Processing LMU Munich Germany Munich Germany Department of Computer Science IT University of Copenhagen Denmark

Despite the success of the Universal Dependencies (UD) project exemplified by its impressive language breadth, there is still a lack in ‘within-language breadth’: most treebanks focus on standard languages. Even for German, the language with the most annotations in UD, so far no treebank exists for one of its language varieties spoken by over 10M people: Bavarian. To contribute to closing this gap, we present the first multi-dialect Bavarian treebank (MaiBaam) manually annotated with part-of-speech and syntactic dependency information in UD, covering multiple text genres (wiki, fiction, grammar examples, social, non-fiction). We highlight the morphosyntactic differences between the closely-related Bavarian and German and showcase the rich variability of speakers’ orthographies. Our corpus includes 15k tokens, covering dialects from all Bavarian-speaking areas spanning three countries. We provide baseline parsing and POS tagging results, which are lower than results obtained on German and vary substantially between different graph-based parsers. To support further research on Bavarian syntax, we make our dataset, language-specific guidelines and code publicly available. © 2024, CC BY-NC-SA.

关键词： Syntactics

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Exploring language-Agnostic speech Representations Using Domain Knowledge for Detecting Alzheimer’s Dementia

Exploring Language-Agnostic Speech Representations Using Dom...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Zehra Shah Shi-Ang Qi Fei Wang Mahtab Farrokh Mashrura Tasnim Eleni Stroulia Russell Greiner Manos Plitsis Athanasios Katsamanis Department of Computing Science University of Alberta Edmonton Canada Institute for Language and Speech Processing Athena Research Center Greece

We explore ways to use speech data to screen for indications of Alzheimer’s dementia (AD). In particular, we describe our approach to the ICASSP 2023 Signal processing Grand Challenge, which involves extrapolating from models learned from English speech samples, to Greek speech samples, to determine which subjects have AD. By using acoustic and linguistic features, inspired by clinical research on AD, our top-performing classification model achieves 69% accuracy in distinguishing AD patients from healthy controls, and our regression model attains an RMSE of 4.8 for inferring cognitive testing scores. These outcomes underscore the potential of our explainable model for detecting cognitive decline in AD patients via speech, and its applicability in clinical settings.

关键词： Signal processing Linguistics Acoustics speech processing Alzheimer's disease Testing

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Incident Task Sequence for Service Priority using Cosine Similarity 1

Incident Task Sequence for Service Priority using Cosine Sim...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Boonprapapan, Teratam Horata, Punyaphol Seresangtakul, Pusadee Natural Language And Speech Processing Laboratory College Of Computing Khon Kaen University Department Of Computer Science Khon Kaen40002 Thailand Advanced Smart Computing Laboratory College Of Computing Khon Kaen University Department Of Computer Science Khon Kaen40002 Thailand

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

The article herein details a procedure for classifying service cases by priority level based on the service level agreement (SLA) between an organization and the customer. The main factor in the article's publication was the accuracy of the classification of the importance of internal service work. However, many service evaluators remain confused about the tiering of service cases. Therefore, creating accurate service case classification models is imperative to simplify the classification process. The service cases consisted of four levels: series, critical, moderate, and low. We employed natural language processing (NLP) to develop a more efficient priority level of service for the organization. We implemented the weighting of the term frequency - inverse document frequency (TF-IDF) method and cosine Similarity with the measuring degree concept of similarity terms within each service case. The model consisted of four processes: data collection, preprocessing, TF-IDF calculation, and similarity and scoring calculation. The model presented here improved the accuracy of the classified process and produced better results in the test sets, measuring the efficiency from the cosine similarity. Lastly, our research contained 5,790 service cases with an accuracy of 70.14%, achieved through the combination of TF-IDF and cosine similarity. © 2022 IEEE.

关键词： Natural language processing systems

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Corrective Retrieval Augmented Generation

arXiv

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arXiv 2024年

作者： Yan, Shi-Qi Gu, Jia-Chen Zhu, Yun Ling, Zhen-Hua National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China Department of Computer Science University of California Los Angeles United States Google DeepMind United Kingdom

Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a practicable complement to LLMs, it relies heavily on the relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. To this end, we propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. Specifically, a lightweight retrieval evaluator is designed to assess the overall quality of retrieved documents for a query, returning a confidence degree based on which different knowledge retrieval actions can be triggered. Since retrieval from static and limited corpora can only return suboptimal documents, large-scale web searches are utilized as an extension for augmenting the retrieval results. Besides, a decompose-then-recompose algorithm is designed for retrieved documents to selectively focus on key information and filter out irrelevant information in them. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches. Experiments on four datasets covering short- and long-form generation tasks show that CRAG can significantly improve the performance of RAG-based approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Structured Query language

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