Answers.Body: <p>The following paper from Amazon Alexa Research refers to this topic as <strong>keyword spotting (KWS)</strong>, and more specifically, to wake up words as <strong>wake word (WW) spotting</strong>.</p>
  <ul>
  <li><a href="https://assets.amazon.science/db/e3/571c26744b2d9c94f77969b1277e/accurate-detection-of-wake-word-start-and-end-using-a-cnn.pdf" rel="nofollow noreferrer">Jose, C., Mishchenko, Y., Senechal, T., Shah, A., Escott, A., Vitaladevuni, S. (2020). Accurate detection of wakeword start and end using a CNN</a></li>
  </ul>
  <p>Some other papers you may find interesting:</p>
  <ul>
  <li><a href="https://ieeexplore.ieee.org/document/9785719?arnumber=9785719" rel="nofollow noreferrer">Luo, M., Wang, D., Wang, X., Qiao, S., &amp; Zhou, Y. (2022). Error-Diffusion Based Speech Feature Quantization for Small-Footprint Keyword Spotting. IEEE Signal Processing Letters, 29, 1357–1361. https://doi.org/10.1109/LSP.2022.3179208</a></li>
  <li><a href="https://arxiv.org/abs/1703.05390" rel="nofollow noreferrer">Arik, S. O., Kliegl, M., Child, R., Hestness, J., Gibiansky, A., Fougner, C., Prenger, R., Coates, A. (2017). Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting. arXiv.</a></li>
  <li><a href="https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42537.pdf" rel="nofollow noreferrer">Chen, G., Parada, C., &amp; Heigold, G. (2014). Small-footprint keyword spotting using deep neural networks. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4087–4091. https://doi.org/10.1109/ICASSP.2014.6854370</a></li>
  <li><a href="https://www.ijcai.org/proceedings/2022/0603.pdf" rel="nofollow noreferrer">Qin, H., Ma, X., Ding, Y., Li, X., Zhang, Y., Tian, Y., Ma, Z., Luo, J., &amp; Liu, X. (2022). BiFSMN: Binary Neural Network for Keyword Spotting. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22)</a></li>
  <li><a href="https://ieeexplore.ieee.org/document/9665618?arnumber=9665618" rel="nofollow noreferrer">Tsai, T. H., &amp; Lin, X. H. (2021). Reduced Model Size Deep Convolutional Neural Networks for Small-Footprint Keyword Spotting. 2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS), 1–4. https://doi.org/10.1109/ICECS53924.2021.9665618</a></li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-09-02T12:45:32.050Z
Answers.LastActivityDate: 2022-09-02T13:08:54.957Z
Answers.LastEditDate: 2022-09-02T13:08:54.957Z
Answers.UserDisplayName: Brian O'Donnell
Body: <p>I have been told that models that are continually running to detect a temporal pattern, say wake up words in Alexa or Echo, the model establishes this <strong>coherence</strong> or another word I heard, <strong>statistical trust</strong>, and does this via classyfying multiple times and looking at the past classifications in some manner.</p>
  <p>I wanted to ask if this methods has got a specific name I can look up? any papers?</p>
  
Tags: <machine-learning><deep-learning><statistics>
Title: Coherence is classifying time series data
UserDisplayName: NeuroEng
ThreadText: # Coherence is classifying time series data
  
  (Asked by: username_0 on 2022-09-01)
  
  I have been told that models that are continually running to detect a temporal pattern, say wake up words in Alexa or Echo, the model establishes this **coherence** or another word I heard, **statistical trust**, and does this via classyfying multiple times and looking at the past classifications in some manner.
  
  I wanted to ask if this methods has got a specific name I can look up? any papers?
  
  **Tags:** machine-learning, deep-learning, statistics
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-09-02. Score: 2
  
  The following paper from Amazon Alexa Research refers to this topic as **keyword spotting (KWS)**, and more specifically, to wake up words as **wake word (WW) spotting**.
  
  * [Jose, C., Mishchenko, Y., Senechal, T., Shah, A., Escott, A., Vitaladevuni, S. (2020). Accurate detection of wakeword start and end using a CNN](https://assets.amazon.science/db/e3/571c26744b2d9c94f77969b1277e/accurate-detection-of-wake-word-start-and-end-using-a-cnn.pdf)
  
  Some other papers you may find interesting:
  
  * [Luo, M., Wang, D., Wang, X., Qiao, S., & Zhou, Y. (2022). Error-Diffusion Based Speech Feature Quantization for Small-Footprint Keyword Spotting. IEEE Signal Processing Letters, 29, 1357–1361. https://doi.org/10.1109/LSP.2022.3179208](https://ieeexplore.ieee.org/document/9785719?arnumber=9785719)
  * [Arik, S. O., Kliegl, M., Child, R., Hestness, J., Gibiansky, A., Fougner, C., Prenger, R., Coates, A. (2017). Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting. arXiv.](https://arxiv.org/abs/1703.05390)
  * [Chen, G., Parada, C., & Heigold, G. (2014). Small-footprint keyword spotting using deep neural networks. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4087–4091. https://doi.org/10.1109/ICASSP.2014.6854370](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42537.pdf)
  * [Qin, H., Ma, X., Ding, Y., Li, X., Zhang, Y., Tian, Y., Ma, Z., Luo, J., & Liu, X. (2022). BiFSMN: Binary Neural Network for Keyword Spotting. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22)](https://www.ijcai.org/proceedings/2022/0603.pdf)
  * [Tsai, T. H., & Lin, X. H. (2021). Reduced Model Size Deep Convolutional Neural Networks for Small-Footprint Keyword Spotting. 2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS), 1–4. https://doi.org/10.1109/ICECS53924.2021.9665618](https://ieeexplore.ieee.org/document/9665618?arnumber=9665618)
  
  
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