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SegAug: CTC-Aligned Segmented Augmentation For Robust RNN-Transducer Based Speech Recognition

SegAug: CTC-Aligned Segmented Augmentation For Robust RNN-Tr...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Khanh Le Tuan Vu Ho Dung Tran Duc Thanh Chau ZaloAI Vietnam Independent Researcher Ho Chi Minh City University of Science Vietnam National University Ho Chi Minh City

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

RNN-Transducer (RNN-T) is a widely adopted architecture in speech recognition, integrating acoustic and language modeling in an end-to-end framework. However, the RNN-T predictor tends to over-rely on consecutive word dependencies in training data, leading to high deletion error rates, particularly with less common or out-of-domain phrases. Existing solutions, such as regularization and data augmentation, often compromise other aspects of performance. We propose SegAug, an alignment-based augmentation technique that generates contextually varied audio-text pairs with low sentence-level semantics. This method encourages the model to focus more on acoustic features while diversifying the learned textual patterns of its internal language model, thereby reducing deletion errors and enhancing overall performance. Evaluations on the LibriSpeech and Tedlium-v3 datasets demonstrate a relative WER reduction of up to 12.5% on small-scale and 6.9% on large-scale settings. Notably, most of the improvement stems from reduced deletion errors, with relative reductions of 45.4% and 18.5%, respectively. These results highlight SegAug’s effectiveness in improving RNN-T’s robustness, offering a promising solution for enhancing speech recognition performance across diverse and challenging scenarios.

关键词： Error analysis Semantics Training data Speech recognition signal processing data augmentation Acoustics Robustness Speech processing

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Comparison of CFAR Algorithms in Real Time Target Detection and Tracking using Millimeter Wave Radar

Comparison of CFAR Algorithms in Real Time Target Detection ...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Vedant Kanawade Camelia Ganguly Manoj Kumar M Valarmathi J Communication Engineering Department Vellore Institute of Technology Vellore India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Recent advancements in millimeter wave (mmwave) radar technology have revolutionized automotive, surveillance, and healthcare applications by providing high-resolution data and robust performance under various environmental conditions. This paper presents a comparative study on two of the widely used Constant False Alarm Rate (CFAR) algorithms, namely Cell Averaging CFAR (CA-CFAR) and Ordered Statistics CFAR (OS-CFAR), which are analyzed for their performance in detecting and tracking targets amidst noise and clutter using BGT60TR13C-Infineon’s 60 GHz mm-wave radar. Furthermore, the study provides insights into optimizing the CFAR parameters for practical deployments, ensuring a balance between detection sensitivity and false alarm rates, ultimately advancing the reliability of next-generation radar systems.

关键词： Technological innovation Target tracking Surveillance signal processing algorithms Radar detection Estimation Millimeter wave radar Radar tracking Radar signal processing Reliability

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A GNSS-IR Aided Multispectral Satellite data Fusion for Meter-Level Wide-Area Volumetric Soil Moisture Estimation

A GNSS-IR Aided Multispectral Satellite Data Fusion for Mete...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Nicolás Padrón Sergiy Vorobyov School of Electrical Engineering Aalto University Espoo Finland Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Earth Observation (EO) data is captured with different instruments and available in multiple formats. The complementation of two passive remote sensing approaches in local small areas is performed here to produce a single, large-area coverage, volumetric soil moisture (VSM) solution. The sensing approaches under consideration are GNSS Interferometric Reflectometry (GNSS-IR), which is a local land-based microwave remote sensing approach that exploits ground-reflected navigation signals; and also multispectral satellite imagery, which is a space-based optical remote sensing approach that allows to analyze the spectral response of surface materials. The focus is on the processing of GNSS-IR outputs aiding a multispectral model using Landsat-8 data. The aim is to provide an accurate, cost-effective solution with wide-area coverage. Landsat-8 spectral indexes highly correlated with soil moisture are fused with GNSS-IR VSM on multiple terrain types. The proposed solution is demonstrated and verified against data from the Soil Moisture Active Passive (SMAP) satellite mission over large-areas.

关键词： Earth Optical interferometry Global navigation satellite system Artificial satellites Satellites Image resolution Soil moisture Estimation Remote sensing signal resolution

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Quantum-Train with Tensor Network Mapping Model and Distributed Circuit Ansatz

Quantum-Train with Tensor Network Mapping Model and Distribu...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chen-Yu Liu Chu-Hsuan Abraham Lin Kuan-Cheng Chen Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of hybrid quantum-classical models. The traditional QT framework employs a multi-layer perceptron (MLP) for this task, but it struggles with scalability and interpretability. To address these issues, we propose replacing the MLP with a tensor network-based model and introducing a distributed circuit ansatz designed for large-scale quantum machine learning with multiple small quantum processing unit nodes. This approach enhances scalability, efficiently represents high-dimensional data, and maintains a compact model structure. Our enhanced QT framework retains the benefits of reduced parameter count and independence from quantum resources during inference. Experimental results on benchmark datasets demonstrate that the tensor network-based QT framework achieves competitive performance with improved efficiency and generalization, offering a practical solution for scalable hybrid quantum-classical machine learning.

关键词： Weight measurement Tensors Quantum computing High dimensional data Scalability Neural networks signal processing Quantum state Integrated circuit modeling Speech processing

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SmartExp: An Adaptive data Expansion Strategy for Improving Handwritten Text Recognition

SmartExp: An Adaptive Data Expansion Strategy for Improving ...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yiming Wang Hongxi Wei Shiwen Sun School of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Constructing a highly accurate handwritten OCR system requires large amounts of high-quality training data, yet data collection is labor-intensive and costly. With the advance of generative models, high-quality synthetic images have been applied to enhance handwritten text recognition (HTR) models, a technique termed data expansion. However, we find that the generated data may sometimes provide only insignificant improvements to HTR. We investigate the causes behind the small gains from the perspectives of both data expansion and data augmentation. We reveal a balancing mechanism in HTR that stronger data expansion should be paired with weaker data augmentation. Informed by these insights, we propose SmartExp, a purely data-driven strategy without introducing any extra data or computational costs. Extensive experiments on three widely used benchmark datasets demonstrate that our SmartExp strategy can significantly improve various HTR models. Our code is publicly available at: https://***/Fyzjym/SmartExp.

关键词： Training Adaptation models Text recognition Computational modeling Training data Speech recognition Benchmark testing data augmentation data models Computational efficiency

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One Shot is Enough for Sequential Infrared small Target Segmentation

One Shot is Enough for Sequential Infrared Small Target Segm...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Bingbing Dan Meihui Li Tao Tang Jing Zhang National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Chengdu China Institute of Optics and Electronics Chinese Academy of Sciences Chengdu China University of Chinese Academy of Sciences Beijing China School of Computing Australian National University Canberra Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Infrared small target sequences exhibit strong similarities between frames and contain rich contextual information, which motivates us to achieve sequential infrared small target segmentation (IRSTS) with minimal data. Inspired by the success of Segment Anything Model (SAM) across various downstream tasks, we propose a one-shot and training-free method that perfectly adapts SAM's zero-shot generalization capability to sequential IRSTS. Specifically, we first obtain a confidence map through local feature matching (LFM). The highest point in the confidence map is used as the prompt to replace the manual prompt. Then, to address the over-segmentation issue caused by the domain gap, we design the point prompt-centric focusing (PPCF) module. Subsequently, to prevent miss and false detections, we introduce the triple-level ensemble (TLE) module to produce the final mask. Experiments demonstrate that our method requires only one shot to achieve comparable performance to state-of-the-art IRSTS methods and significantly outperforms other one-shot segmentation methods. Moreover, ablation studies confirm the robustness of our method in the type of annotations and the selection of reference images. Our codes are available at https://***/D-IceIce/one-shot-IRSTS.

关键词： Image segmentation Adaptation models Codes Annotations Focusing Manuals signal processing Robustness Acoustics Speech processing

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PromptSeg: Learning to Segment Medical Image via Visual Prompts

PromptSeg: Learning to Segment Medical Image via Visual Prom...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Minfan Zhao Ziqi Zhu Jun Shi Zhaohui Wang Junshi Chen Hong An Bing Yan School of Computer Science and Technology University of Science and Technology of China Hefei China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China The First Affiliated Hospital of USTC University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning has made remarkable medical image segmentation advancements, yet its generalization capability across tasks remains challenging. The variety of task objectives, disease-dependent labeling variations, and multi-center data contribute to the poor generalization capacity of task-specific models on unseen tasks, necessitating domain adaptation or fine-tuning. This typically involves data annotation and network retraining, limiting the application of deep learning in clinical practice. In this study, we propose PromptSeg, an innovative Transformer-based unified segmentation framework for general medical image segmentation tasks. PromptSeg aims to utilize the provided visual prompts to recognize task patterns and learn contextual representations, thereby breaking the restrictions of the task-specific paradigm. When faced with unseen datasets or segmentation targets during inference, our method only requires a few annotated prompt pairs to understand the task and segment the query images without retraining, alleviating the need for large-scale annotation data. The experimental results demonstrate that our method outperforms existing state-of-the-art methods and exhibits high generalization capability on multiple unseen tasks. The source code is available at https://***/MinfanZhao/PromptSeg.

关键词： Deep learning Training Image segmentation Visualization Annotations Source coding Scalability Transformers Speech processing Biomedical imaging

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Denoising and Restoring Channel State Information for 5G Indoor Positioning in Low-SNR Scenarios

Denoising and Restoring Channel State Information for 5G Ind...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jianing Chen Junze Yang Chuhao Chen Xiangxu Meng Wenqi Zheng College of Computer Science and Technology Harbin Engineering University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Indoor positioning with 5G technologies depends on accurate Channel State Information (CSI) for high-precision location services, but limited data, especially in low signal-to-noise ratio (SNR) environments, presents a significant challenge. Traditional deep learning methods, relying on "Learning + Finetuning", require large datasets at specific SNR levels, limiting their effectiveness. This paper introduces a novel CSI image generation and restoration method designed for small-sample conditions, allowing data from various SNR levels to be utilized to improve downstream task performance. We propose a new CSI generation model that leverages graph neural networks and a multi-level loss function to accurately generate CSI images corresponding to specific SNRs. Our approach addresses the unique characteristics of CSI signals that traditional image generation methods fail to capture. Experiments show that our approach significantly improves prediction accuracy, outperforming existing methods, particularly in low-SNR scenarios. Our core code is publically available at https://***/kaqiz/DARC.

关键词： Convolutional codes Accuracy 5G mobile communication Image synthesis Convolution Noise reduction Image restoration Speech processing Channel state information signal to noise ratio

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Lightweight neural front-ends for low-resource on-device Text-to-Speech

Lightweight neural front-ends for low-resource on-device Tex...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Giulia Comini Heereen Shim Manuel Sam Ribeiro Amazon AGI United Kingdom Amazon AGI Netherlands Amazon AGI Poland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We propose a lightweight neural front-end framework for on-device speech generation and highlight its benefits towards low-resource language scaling. While data-driven models have shown potential in front-end literature, especially since they can enable fast language expansion, they are often extremely large and of high latency. There is limited work focusing on their usability in real-time settings, and none for on-device TTS applications. At the cost of small performances trade-offs, we build lightweight neural Grapheme-to-Phoneme and verbalization models which achieve, on average across three languages, a p90 latency reduction of 95.98% per token on single-threaded CPU@2.30GHz, with respect to a traditional transformer-based baseline, while having 99.26% less parameters. Additionally, leveraging pre-trained teacher models to bootstrap lightweight students, we enable low-resource language scaling on both Grapheme-to-Phoneme conversion and verbalization.

关键词： Costs Focusing signal processing Transformers Real-time systems Acoustics Text to speech Usability Speech processing

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Audiopedia: Audio QA with Knowledge

Audiopedia: Audio QA with Knowledge

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Abhirama Subramanyam Penamakuri Kiran Chhatre Akshat Jain Indian Institute of Technology Jodhpur KTH Royal Institute of Technology Sweden

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we introduce Audiopedia, a novel task called Audio Question Answering with Knowledge, which requires both audio comprehension and external knowledge reasoning. Unlike traditional Audio Question Answering (AQA) benchmarks that focus on simple queries answerable from audio alone, Audiopedia targets knowledge-intensive questions. We define three sub-tasks: (i) Single Audio Question Answering (s-AQA), where questions are answered based on a single audio sample, (ii) Multi-Audio Question Answering (m-AQA), which requires reasoning over multiple audio samples, and (iii) Retrieval-Augmented Audio Question Answering (r-AQA), which involves retrieving relevant audio to answer the question. We benchmark large audio language models (LALMs) on these sub-tasks and observe suboptimal performance. To address this, we propose a generic framework that can be adapted to any LALM, equipping them with knowledge reasoning capabilities. Our framework has two components: (i) Audio Entity Linking (AEL) and (ii) Knowledge-Augmented Audio Large Multimodal Model (KA 2 LM), which together improve performance on knowledge-intensive AQA tasks. To our knowledge, this is the first work to address advanced speech audio understanding through knowledge-intensive tasks like Audiopedia. We make our data publicly available: https://***/projects/audiopedia/.

关键词： Adaptation models Benchmark testing signal processing Question answering (information retrieval) Cognition Acoustics Speech processing

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