• 最近沉迷于語音喚醒，順便在學(xué)術(shù)界上把語音喚醒摸個(gè)底，稍后可能放出語音喚醒的相關(guān)調(diào)研報(bào)告
• 帶鏈接的都是有源碼的
• 按照時(shí)間線劃分

第一部分 來自arXiv

arXiv 中搜索關(guān)鍵詞 “Small-footprint Keyword Spotting” 的 2018 - 2020 的paper

arXiv:2002.10851 [pdf, other]
Small-Footprint Open-Vocabulary Keyword Spotting with Quantized LSTM Networks

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arXiv:1912.07575 [pdf, other] cs.CL cs.LG
Predicting detection filters for small footprint open-vocabulary keyword spotting

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arXiv:1912.05124 [pdf, other] cs.SD cs.CL cs.LG eess.AS
Small-footprint Keyword Spotting with Graph Convolutional Network

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arXiv:1911.02086 [pdf, other] eess.AS cs.CL cs.SD
Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions

https://paperswithcode.com/paper/small-footprint-keyword-spotting-on-raw-audio

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arXiv:1910.05171 [pdf, other] cs.LG cs.CL eess.AS stat.ML
Query-by-example on-device keyword spotting

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arXiv:1907.01448 [pdf, other] eess.AS cs.SD
Sub-band Convolutional Neural Networks for Small-footprint Spoken Term Classification

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arXiv:1906.09417 [pdf, other] cs.SD cs.HC cs.LG eess.AS
Keyword Spotting for Hearing Assistive Devices Robust to External Speakers

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arXiv:1906.08415 [pdf, other] cs.SD cs.LG cs.MM eess.AS
A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting

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arXiv:1811.07684 [pdf, other] cs.LG cs.CL cs.SD eess.AS stat.ML
Efficient keyword spotting using dilated convolutions and gating

https://paperswithcode.com/paper/efficient-keyword-spotting-using-dilated

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arXiv:1811.00348 [pdf, ps, other] cs.SD eess.AS
Sequence-to-sequence Models for Small-Footprint Keyword Spotting

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arXiv:1803.10916 [pdf, other] cs.SD cs.CL eess.AS
Attention-based End-to-End Models for Small-Footprint Keyword Spotting

第二部分

知乎、論文、簡書中摘取

2019年

• Temporal Convolution for Real-time Keyword Spotting on Mobile Devices
  • https://paperswithcode.com/paper/temporal-convolution-for-real-time-keyword

2018年

• Shan, et al., “Attention-based end-to-end models for small-footprint keyword spotting”, Interspeech, 2018. 注意力
• Zhang H, Zhang J, Wang Y. Sequence-to-sequence models for small-footprint keywordspotting[J]. arXiv preprint arXiv:1811.00348, 2018.
  • 基于序列到序列的喚醒詞識(shí)別模型
• Deep residual learning for small-footprint keyword spotting[C].IEEE InternationalConference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Calgary, AB, Canada,Apr.15-20, 2018: 5484-5488
  • https://paperswithcode.com/paper/deep-residual-learning-for-small-footprint
  • 深度殘差學(xué)習(xí)和擴(kuò)展卷積的喚醒詞識(shí)別方法

2017 年

• Audhkhasi, et al., “End-to-end ASR-free keyword search from speech”, ICASSP, 2017.
  • 使用一個(gè) CRNN 語言模型把喚醒詞編碼成一個(gè)嵌入向量。
• Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting
  • https://paperswithcode.com/paper/honk-a-pytorch-reimplementation-of
• He, et al., “Streaming small-footprint keyword spotting using sequence-to-sequence models”, ASRU, 2017.
  • 基于 RNN 的端到端訓(xùn)練的序列到序列的喚醒詞模型
• Ar?k, et al., “Convolutional recurrent neural networks for small-footprint keyword spotting”, arxiv:1703.05390. 百度
• 基于CRNN 的喚醒詞識(shí)別方法
• Hello Edge: Keyword Spotting on Microcontrollers
  • https://paperswithcode.com/paper/hello-edge-keyword-spotting-on
• F. Ge and Y. Yan, “Deep neural network based wake-up-word speech recognition with two-stage detection”, ICASSP, 2017.
  • 固定長度的嵌入向量，用序列形式
  • 基于DNN的兩階段檢測的喚醒詞識(shí)別系統(tǒng)
• Compressed time delay neural network for small-footprint keyword spotting - 2017 INTERSPEECH
  • 為了解決 DNN 帶來的搜索延遲和低階特性
  • 低秩權(quán)重矩陣改進(jìn)了 DNN 網(wǎng)絡(luò) 23
• Kumatani, et al., “Direct modeling of raw audio with DNNs for wake word detection”, ASRU, 2017.
• 提取MFCC特征通過DNN進(jìn)行訓(xùn)練，類似的有陳果果2014

2016年

• Sun M, Raju A, Tucker G, et al. Max-pooling loss training of long short-term memory networksfor small-footprint keyword spotting[C].IEEE Spoken Language Technology Workshop (SLT).IEEE, San Diego, CA, USA, Dec.13-16, 2016: 474-480.
  • 用后驗(yàn)平滑的評估 方法估計(jì)喚醒詞識(shí)別性能
  • 最大池化的損失函數(shù)訓(xùn)練 LSTM 網(wǎng)絡(luò)
• “Investigating neural network based query-by-example keyword spotting approach for personalized wake-up word detection in Mandarin Chinese”, Int’l Symposium on Chinese Spoken Language Processing, 2016.
  • 提出模板匹配，LSTM提取特征，固定長度和特征向量

2015年

• T. N. Sainath and C. Parada, “Convolutional neural networks for small-footprint keyword spotting”, Interspeech, 2015.
  • 基于 CNN 的喚醒詞識(shí)別的方法
• Chen, et al., “Query-by-example keyword spotting using long short-term memory networks”, ICASSP, 2015.
• 先用神經(jīng)網(wǎng)絡(luò)提取特征然后用時(shí)間動(dòng)態(tài)規(guī)整對喚醒詞進(jìn)行判斷

2014年

• G. Chen, et al., “Small-footprint keyword spotting using deep neural networks”, ICASSP, 2014.
  • 經(jīng)典，DNN，陳果果，拜讀

other 往前就是傳統(tǒng)的文章了，暫時(shí)不建議閱讀

• 2006年，提出喚醒詞和喚醒詞識(shí)別
• 2009年，韻律特征研究
• HMM 訓(xùn)練聲學(xué)模型，用SVM劃分是否喚醒詞
• 動(dòng)態(tài)時(shí)間規(guī)整算法
  • 模板匹配，距離測量
  • 麥克風(fēng)陣列檢測喚醒詞
• 2014年，嵌入式平臺(tái)的喚醒詞識(shí)別系統(tǒng)開發(fā)

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