Built on

Hochreiter, S., Schmidhuber, J., Nov. 1997. Long short-term memory. Neural Comput. 9 (8), 1735–1780. URL http://dx.doi.org/10.1162/neco.1997.9.8.1735
1997
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Sundberg, J., LÃ£, F. M., Gill, B. P., 2013. Formant tuning strategies in professional male opera singers. Journal of Voice 27 (3), 278 – 288. URL http://www.sciencedirect.com/science/article/pii/S0892199712002093
2013
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Tabibian, S., Akbari, A., Nasersharif, B., 2013. Keyword spotting using an evolutionary-based classifier and discriminative features. Engineering Applications of Artificial Intelligence 26 (7), 1660 – 1670. URL http://www.sciencedirect.com/science/article/pii/S0952197613000511
2013
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Bahdanau, D., Cho, K., Bengio, Y., 2014. Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473. URL http://arxiv.org/abs/1409.0473
Original
2014
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Kingma, D. P., Ba, J., 2014. Adam: A method for stochastic optimization. CoRR abs/1412.6980. URL http://arxiv.org/abs/1412.6980
Original
2014
Earlier work this paper cites.
Sangeetha, J., Jothilakshmi, S., 2014. A novel spoken keyword spotting system using support vector machine. Engineering Applications of Artificial Intelligence 36, 287 – 293. URL http://www.sciencedirect.com/science/article/pii/S0952197614001821
2014
Earlier work this paper cites.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. URL https://www.tensorflow.org/
2015
Earlier work this paper cites.
Amodei, D., Anubhai, R., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Chen, J., Chrzanowski, M., Coates, A., Diamos, G., Elsen, E., Engel, J., Fan, L., Fougner, C., Han, T., Hannun, A. Y., Jun, B., LeGresley, P., Lin, L., Narang, S., Ng, A. Y., Ozair, S., Prenger, R., Raiman, J., Satheesh, S., Seetapun, D., Sengupta, S., Wang, Y., Wang, Z., Wang, C., Xiao, B., Yogatama, D., Zhan, J., Zhu, Z., 2015. Deep speech 2: End-to-end speech recognition in english and mandarin. CoRR abs/1512.02595. URL http://arxiv.org/abs/1512.02595
Original
2015
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Chollet, F., et al., 2015. Keras. URL https://keras.io
2015
Earlier work this paper cites.

Similar

Sainath, T. N., Parada, C., 2015. Convolutional neural networks for small-footprint keyword spotting. In: INTERSPEECH
2015
Cited alongside, same era.
Sundberg, J., ThalÃ©n, M., 2015. Respiratory and acoustical differences between belt and neutral style of singing. Journal of Voice 29 (4), 418 – 425. URL http://www.sciencedirect.com/science/article/pii/S0892199714002045
2015
Cited alongside, same era.
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A. C., Salakhutdinov, R., Zemel, R. S., Bengio, Y., 2015. Show, attend and tell: Neural image caption generation with visual attention. CoRR abs/1502.03044. URL http://arxiv.org/abs/1502.03044
Original
2015
Cited alongside, same era.
Arik, S. Ö., Kliegl, M., Child, R., Hestness, J., Gibiansky, A., Fougner, C., Prenger, R., Coates, A., 2017. Convolutional recurrent neural networks for small-footprint keyword spotting. CoRR abs/1703.05390. URL http://arxiv.org/abs/1703.05390
Original
2017
Cited alongside, same era.

Then

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., Polosukhin, I., 2017. Attention is all you need. CoRR abs/1706.03762. URL http://arxiv.org/abs/1706.03762
Original
2017
Later among the works it cites.
Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S., Le, Q. V., Agiomyrgiannakis, Y., Clark, R., Saurous, R. A., 2017. Tacotron: A fully end-to-end text-to-speech synthesis model. CoRR abs/1703.10135. URL http://arxiv.org/abs/1703.10135
Original
2017
Later among the works it cites.
Zhang, Y., Suda, N., Lai, L., Chandra, V., 2017. Hello edge: Keyword spotting on microcontrollers. CoRR abs/1711.07128. URL http://arxiv.org/abs/1711.07128
Original
2017
Later among the works it cites.
Chen, Z., Qian, Y., Yu, K., 2018. Sequence discriminative training for deep learning based acoustic keyword spotting. Speech Communication 102, 100 – 111. URL http://www.sciencedirect.com/science/article/pii/S0167639317303631
2018

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2018

A neural attention model for speech command recognition

de Andrade, Douglas Coimbra, Leo, Sabato, Viana, Martin Loesener Da Silva et al.

Understand

This paper introduces a convolutional recurrent network with attention for speech command recognition.

Attention models are powerful tools to improve performance on natural language, image captioning and speech tasks.
The proposed model establishes a new state-of-the-art accuracy of 94.1% on Google Speech Commands dataset V1 and 94.5% on V2 (for the 20-commands recognition task), while still keeping a small footprint of only 202K trainable parameters.
Results are compared with previous convolutional implementations on 5 different tasks (20 commands recognition (V1 and V2), 12 commands recognition (V1), 35 word recognition (V1) and left-right (V1)).

Built on

Hochreiter, S., Schmidhuber, J., Nov. 1997. Long short-term memory. Neural Comput. 9 (8), 1735–1780. URL http://dx.doi.org/10.1162/neco.1997.9.8.1735
1997
Earlier work this paper cites.
Sundberg, J., LÃ£, F. M., Gill, B. P., 2013. Formant tuning strategies in professional male opera singers. Journal of Voice 27 (3), 278 – 288. URL http://www.sciencedirect.com/science/article/pii/S0892199712002093
2013
Earlier work this paper cites.
Tabibian, S., Akbari, A., Nasersharif, B., 2013. Keyword spotting using an evolutionary-based classifier and discriminative features. Engineering Applications of Artificial Intelligence 26 (7), 1660 – 1670. URL http://www.sciencedirect.com/science/article/pii/S0952197613000511
2013
Earlier work this paper cites.
Bahdanau, D., Cho, K., Bengio, Y., 2014. Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473. URL http://arxiv.org/abs/1409.0473
Original
2014
Earlier work this paper cites.
Kingma, D. P., Ba, J., 2014. Adam: A method for stochastic optimization. CoRR abs/1412.6980. URL http://arxiv.org/abs/1412.6980
Original
2014
Earlier work this paper cites.
Sangeetha, J., Jothilakshmi, S., 2014. A novel spoken keyword spotting system using support vector machine. Engineering Applications of Artificial Intelligence 36, 287 – 293. URL http://www.sciencedirect.com/science/article/pii/S0952197614001821
2014
Earlier work this paper cites.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. URL https://www.tensorflow.org/
2015
Earlier work this paper cites.
Amodei, D., Anubhai, R., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Chen, J., Chrzanowski, M., Coates, A., Diamos, G., Elsen, E., Engel, J., Fan, L., Fougner, C., Han, T., Hannun, A. Y., Jun, B., LeGresley, P., Lin, L., Narang, S., Ng, A. Y., Ozair, S., Prenger, R., Raiman, J., Satheesh, S., Seetapun, D., Sengupta, S., Wang, Y., Wang, Z., Wang, C., Xiao, B., Yogatama, D., Zhan, J., Zhu, Z., 2015. Deep speech 2: End-to-end speech recognition in english and mandarin. CoRR abs/1512.02595. URL http://arxiv.org/abs/1512.02595
Original
2015
Earlier work this paper cites.
Chollet, F., et al., 2015. Keras. URL https://keras.io
2015
Earlier work this paper cites.

Similar

Sainath, T. N., Parada, C., 2015. Convolutional neural networks for small-footprint keyword spotting. In: INTERSPEECH
2015
Cited alongside, same era.
Sundberg, J., ThalÃ©n, M., 2015. Respiratory and acoustical differences between belt and neutral style of singing. Journal of Voice 29 (4), 418 – 425. URL http://www.sciencedirect.com/science/article/pii/S0892199714002045
2015
Cited alongside, same era.
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A. C., Salakhutdinov, R., Zemel, R. S., Bengio, Y., 2015. Show, attend and tell: Neural image caption generation with visual attention. CoRR abs/1502.03044. URL http://arxiv.org/abs/1502.03044
Original
2015
Cited alongside, same era.
Arik, S. Ö., Kliegl, M., Child, R., Hestness, J., Gibiansky, A., Fougner, C., Prenger, R., Coates, A., 2017. Convolutional recurrent neural networks for small-footprint keyword spotting. CoRR abs/1703.05390. URL http://arxiv.org/abs/1703.05390
Original
2017
Cited alongside, same era.

Then

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., Polosukhin, I., 2017. Attention is all you need. CoRR abs/1706.03762. URL http://arxiv.org/abs/1706.03762
Original
2017
Later among the works it cites.
Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S., Le, Q. V., Agiomyrgiannakis, Y., Clark, R., Saurous, R. A., 2017. Tacotron: A fully end-to-end text-to-speech synthesis model. CoRR abs/1703.10135. URL http://arxiv.org/abs/1703.10135
Original
2017
Later among the works it cites.
Zhang, Y., Suda, N., Lai, L., Chandra, V., 2017. Hello edge: Keyword spotting on microcontrollers. CoRR abs/1711.07128. URL http://arxiv.org/abs/1711.07128
Original
2017
Later among the works it cites.
Chen, Z., Qian, Y., Yu, K., 2018. Sequence discriminative training for deep learning based acoustic keyword spotting. Speech Communication 102, 100 – 111. URL http://www.sciencedirect.com/science/article/pii/S0167639317303631
2018

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Chiu, C., Sainath, T. N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R. J., Rao, K., Gonina, K., Jaitly, N., Li, B., Chorowski, J., Bacchiani, M., 2017. State-of-the-art speech recognition with sequence-to-sequence models. CoRR abs/1712.01769. URL http://arxiv.org/abs/1712.01769

Original

2017

Cited alongside, same era.

Choi, K., Joo, D., Kim, J., 2017. Kapre: On-gpu audio preprocessing layers for a quick implementation of deep neural network models with keras. In: Machine Learning for Music Discovery Workshop at 34th International Conference on Machine Learning. ICML

2017

Cited alongside, same era.

McMahan, B., Rao, D., 2017. Listening to the world improves speech command recognition. CoRR abs/1710.08377. URL http://arxiv.org/abs/1710.08377

Original

2017

Cited alongside, same era.

Tang, R., Lin, J., 2017. Deep residual learning for small-footprint keyword spotting. CoRR abs/1710.10361. URL http://arxiv.org/abs/1710.10361

Original

2017

Cited alongside, same era.

Closest in time.

Google Brain, 2018. Tensorflow speech recognition challenge. URL https://www.kaggle.com/c/tensorflow-speech-recognition-challenge/

2018

Closest in time.

Jansson, P., 2018. Single-word speech recognition with convolutional neural networks on raw waveforms. URL https://www.theseus.fi/bitstream/handle/10024/144982/Jansson_Patrick.pdf?sequence=1

2018

Closest in time.

Lei, D., Chen, X., Zhao, J., 2018. Opening the black box of deep learning. CoRR abs/1805.08355. URL http://arxiv.org/abs/1805.08355

Original

2018

Closest in time.

Warden, P., 2018. Speech commands: A dataset for limited-vocabulary speech recognition. CoRR abs/1804.03209. URL http://arxiv.org/abs/1804.03209

Original

2018

Closest in time.