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语音唤醒论文待看

發(fā)布時(shí)間：2024/3/13 编程问答 38 豆豆

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最近沉迷于語(yǔ)音喚醒，順便在學(xué)術(shù)界上把語(yǔ)音喚醒摸個(gè)底，稍后可能放出語(yǔ)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

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

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

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

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

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

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

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

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

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

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

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

知乎、論文、簡(jiǎn)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(yǔ)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(zhǎng)度的嵌入向量，用序列形式
- 基于DNN的兩階段檢測(cè)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(lái)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(guò)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(píng)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(zhǎng)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(duì)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

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