Pengcheng Guo

ORCID: 0009-0008-7069-979X
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About
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Research Areas
  • Advanced Neural Network Applications
  • Infrared Target Detection Methodologies
  • Speech Recognition and Synthesis
  • Voice and Speech Disorders
  • Advanced SAR Imaging Techniques
  • Industrial Vision Systems and Defect Detection
  • Natural Language Processing Techniques
  • Speech and Audio Processing
  • Tracheal and airway disorders
  • Dysphagia Assessment and Management
  • Music and Audio Processing
  • Phonetics and Phonology Research

China North Industries Group Corporation (China)
2025

Northwestern Polytechnical University
2024

Dalian Maritime University
2024

The availability of high-quality and ample synthetic aperture radar (SAR) image datasets is crucial for understanding recognizing target characteristics. However, in practical applications, the limited SAR images significantly impedes advancement interpretation methodologies. In this study, we introduce a Generative Adversarial Network (GAN)-based approach designed to manipulate azimuth angle with few samples, thereby generating adjustable ranges. proposed method consists three modules:...

10.3390/rs17071206 article EN cc-by Remote Sensing 2025-03-28

While automatic speech recognition (ASR) systems degrade significantly in noisy environments, audio-visual (AVSR) aim to complement the audio stream with noise-invariant visual cues and improve system's robustness. However, current studies mainly focus on fusing well-learned modality features, like output of modality-specific encoders, without considering contextual relationship during feature learning. In this study, we propose a multi-layer cross-attention fusion based AVSR (MLCA-AVSR)...

10.48550/arxiv.2401.03424 preprint EN other-oa arXiv (Cornell University) 2024-01-01

Speech has emerged as a widely embraced user interface across diverse applications. However, for individuals with dysarthria, the inherent variability in their speech poses significant challenges. This paper presents an end-to-end Pretrain-based Dual-filter Dysarthria Wake-up word Spotting (PD-DWS) system SLT 2024 Low-Resource Wake-Up Word Challenge. Specifically, our improves performance from two key perspectives: audio modeling and dual-filter strategy. For modeling, we propose innovative...

10.48550/arxiv.2409.10076 preprint EN arXiv (Cornell University) 2024-09-16
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