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2018 | OriginalPaper | Chapter

Gated Convolutional LSTM for Speech Commands Recognition

Authors : Dong Wang, Shaohe Lv, Xiaodong Wang, Xinye Lin

Published in: Computational Science – ICCS 2018

Publisher: Springer International Publishing

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Abstract

As the mobile device gaining increasing popularity, Acoustic Speech Recognition on it is becoming a leading application. Unfortunately, the limited battery and computational resources on a mobile device highly restrict the potential of Speech Recognition systems, most of which have to resort to a remote server for better performance. To improve the performance of local Speech Recognition, we propose C-1-G-2-Blstm. This model shares Convolutional Neural Network’s ability of learning local feature and Recurrent Neural Network’s ability of learning sequence data’s long dependence. Furthermore, by adopting the Gated Convolutional Neural Network instead of a traditional CNN, we manage to greatly improve the model’s capacity. Our tests demonstrate that C-1-G-2-Blstm can achieve a high accuracy at 90.6% on the Google Speech Commands data set, which is 6.4% higher than the state-of-art methods.

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Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., et al.: Deep speech 2: end-to-end speech recognition in English and mandarin. In: International Conference on Machine Learning, pp. 173–182 (2016)

Arik, S.O., Kliegl, M., Child, R., Hestness, J., Gibiansky, A., Fougner, C., Prenger, R., Coates, A.: Convolutional recurrent neural networks for small-footprint keyword spotting. arXiv preprint arXiv:1703.05390 (2017)

Chen, G., Parada, C., Heigold, G.: Small-footprint keyword spotting using deep neural networks. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4087–4091. IEEE (2014)

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

Hannun, A., Case, C., Casper, J., Catanzaro, B., Diamos, G., Elsen, E., Prenger, R., Satheesh, S., Sengupta, S., Coates, A., et al.: Deep speech: scaling up end-to-end speech recognition. arXiv preprint arXiv:1412.5567 (2014)

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

Hershey, S., Chaudhuri, S., Ellis, D.P., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., et al.: CNN architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135. IEEE (2017)

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

10.

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef

11.

McMahan, B., Rao, D.: Listening to the world improves speech command recognition. arXiv preprint arXiv:1710.08377 (2017)

12.

van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with PixelCNN decoders. In: Advances in Neural Information Processing Systems, pp. 4790–4798 (2016)

13.

Sainath, T.N., Kingsbury, B., Mohamed, A.r., Dahl, G.E., Saon, G., Soltau, H., Beran, T., Aravkin, A.Y., Ramabhadran, B.: Improvements to deep convolutional neural networks for LVCSR. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 315–320. IEEE (2013)

14.

Sainath, T.N., Parada, C.: Convolutional neural networks for small-footprint keyword spotting. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)

15.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

16.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

17.

Tang, R., Lin, J.: Deep residual learning for small-footprint keyword spotting. arXiv preprint arXiv:1710.10361 (2017)

18.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is All You Need. arXiv e-prints, June 2017

19.

Wang, Y., Getreuer, P., Hughes, T., Lyon, R.F., Saurous, R.A.: Trainable frontend for robust and far-field keyword spotting. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5670–5674. IEEE (2017)

20.

Warden, P.: Launching the speech commands dataset. Google Research Blog (2017)

21.

Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)

22.

Zhang, Y., Pezeshki, M., Brakel, P., Zhang, S., Bengio, C.L.Y., Courville, A.: Towards end-to-end speech recognition with deep convolutional neural networks. arXiv preprint arXiv:1701.02720 (2017)

23.

Zhang, Y., Chan, W., Jaitly, N.: Very deep convolutional networks for end-to-end speech recognition. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4845–4849. IEEE (2017)

Title: Gated Convolutional LSTM for Speech Commands Recognition
Authors: Dong Wang
Shaohe Lv
Xiaodong Wang
Xinye Lin
Publisher: Springer International Publishing
Book: Computational Science – ICCS 2018
Print ISBN: 978-3-319-93700-7

Electronic ISBN: 978-3-319-93701-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-93701-4_53

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