mlsanityData to 14 Oct 2026

Advancing Airport Tower Command Recognition: Integrating Squeeze-and-Excitation and Broadcasted Residual Learning

arXiv 2406.18313 ↗First posted 26 Jun 2024cs.SD cs.CL eess.AScited by 0

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Abstract (arXiv)

Accurate recognition of aviation commands is vital for flight safety and efficiency, as pilots must follow air traffic control instructions precisely. This paper addresses challenges in speech command recognition, such as noisy environments and limited computational resources, by advancing keyword spotting technology. We create a dataset of standardized airport tower commands, including routine and emergency instructions. We enhance broadcasted residual learning with squeeze-and-excitation and time-frame frequency-wise squeeze-and-excitation techniques, resulting in our BC-SENet model. This model focuses on crucial information with fewer parameters. Our tests on five keyword spotting models, including BC-SENet, demonstrate superior accuracy and efficiency. These findings highlight the effectiveness of our model advancements in improving speech command recognition for aviation safety and efficiency in noisy, high-stakes environments. Additionally, BC-SENet shows comparable performance on the common Google Speech Command dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 27 references that are in the map. First posted on arXiv 26 Jun 2024.

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21 of 27 references.

References · 21

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Squeeze-and-Excitation Networkscited by 4,789
  3. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  4. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 752
  5. 2022Visual Attention Networkcited by 210
  6. 2021Keyword Transformer: A Self-Attention Model for Keyword Spottingcited by 60
  7. 2019An Empirical Study of Spatial Attention Mechanisms in Deep Networkscited by 58
  8. 2019Temporal Convolution for Real-time Keyword Spotting on Mobile Devicescited by 49
  9. 2021Broadcasted Residual Learning for Efficient Keyword Spottingcited by 48
  10. 2021Deep Spoken Keyword Spotting: An Overviewcited by 43
  11. 2020MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognitioncited by 36
  12. 2020Depthwise Separable Convolutional ResNet with Squeeze-and-Excitation Blocks for Small-footprint Keyword Spottingcited by 15
9 more
  1. 2021SubSpectral Normalization for Neural Audio Data Processingcited by 11
  2. 2022ConvMixer: Feature Interactive Convolution with Curriculum Learning for Small Footprint and Noisy Far-field Keyword Spottingcited by 11
  3. 2020Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification using CTC-based Soft VAD and Global Query Attentioncited by 9
  4. 2022Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spottingcited by 8
  5. 2021ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systemscited by 6
  6. 2022Continual Learning For On-Device Environmental Sound Classificationcited by 4
  7. 2023Frequency & Channel Attention for Computationally Efficient Sound Event Detectioncited by 3
  8. 2022Convolution-Based Channel-Frequency Attention for Text-Independent Speaker Verificationcited by 2
  9. 2024Dual Knowledge Distillation for Efficient Sound Event Detectioncited by 2

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