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Personalization of on-device speech recognition (ASR) has seen explosive growth in recent years, largely due to the increasing popularity of personal assistant features on mobile devices and smart home speakers.
E. C. Cherry, “Some experiments on the recognition of speech, with one and with two ears,” The Journal of the acoustical society of America , vol. 25, no. 5, pp. 975–979, 1953
1953
Earlier work this paper cites.
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Johnson, B. Hariharan, L. Van Der Maaten, L. Fei-Fei, C. Lawrence Zitnick, and R. Girshick, “Clevr: A diagnostic dataset for compositional language and elementary visual reasoning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2901–2910
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
C. Kim, A. Misra, K. Chin, T. Hughes, A. Narayanan, T. Sainath, and M. Bacchiani, “Generation of large-scale simulated utterances in virtual rooms to train deep-neural networks for far-field speech recognition in Google Home,” in Interspeech , 2017, pp. 379–383
2017
Earlier work this paper cites.
V. Dumoulin, E. Perez, N. Schucher, F. Strub, H. d. Vries, A. Courville, and Y. Bengio, “Feature-wise transformations,” Distill , vol. 3, no. 7, p. e11, 2018
2018
Earlier work this paper cites.
L. Wan, Q. Wang, A. Papir, and I. L. Moreno, “Generalized end-to-end loss for speaker verification,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 4879–4883
2018
Earlier work this paper cites.
E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville, “Film: Visual reasoning with a general conditioning layer,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
L. Dong, S. Xu, and B. Xu, “Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 5884–5888
2018
Cited alongside, same era.
Y. He, T. N. Sainath, R. Prabhavalkar, I. McGraw, R. Alvarez, D. Zhao, D. Rybach, A. Kannan, Y. Wu, R. Pang et al. , “Streaming end-to-end speech recognition for mobile devices,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 6381–6385
2019
Cited alongside, same era.
N. Li, S. Liu, Y. Liu, S. Zhao, and M. Liu, “Neural speech synthesis with transformer network,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 6706–6713
2019
Cited alongside, same era.
C. Wang, Y. Wu, L. Lu, S. Liu, J. Li, G. Ye, and M. Zhou, “Low latency end-to-end streaming speech recognition with a scout network,” Proc. Interspeech , pp. 2112–2116, 2020
2020
Later among the works it cites.
A. Gulati, J. Qin, C.-C. Chiu, N. Parmar, Y. Zhang, J. Yu, W. Han, S. Wang, Z. Zhang, Y. Wu et al. , “Conformer: Convolution-augmented transformer for speech recognition,” Proc. Interspeech , pp. 5036–5040, 2020
2020
Later among the works it cites.
N. Moritz, T. Hori, and J. Le, “Streaming automatic speech recognition with the transformer model,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6074–6078
2020
Later among the works it cites.
2021
Later among the works it cites.
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2019
Cited alongside, same era.
D. S. Park, W. Chan, Y. Zhang, C.-C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le, “Specaugment: A simple data augmentation method for automatic speech recognition,” Proc. Interspeech , pp. 2613–2617, 2019
2019
Cited alongside, same era.
S. Ding, Q. Wang, S.-Y. Chang, L. Wan, and I.-L. Moreno, “Personal VAD: Speaker-conditioned voice activity detection,” in Proc. Odyssey 2020 The Speaker and Language Recognition Workshop , 2020, pp. 433–439
2020
Cited alongside, same era.
I. Medennikov, M. Korenevsky, T. Prisyach, Y. Khokhlov, M. Korenevskaya, I. Sorokin, T. Timofeeva, A. Mitrofanov, A. Andrusenko, I. Podluzhny et al. , “Target-speaker voice activity detection: a novel approach for multi-speaker diarization in a dinner party scenario,” in Proc. Interspeech , 2020
2020
Cited alongside, same era.
T. N. Sainath, Y. He, B. Li, A. Narayanan, R. Pang, A. Bruguier, S.-y. Chang, W. Li, R. Alvarez, Z. Chen et al. , “A streaming on-device end-to-end model surpassing server-side conventional model quality and latency,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6059–6063
2020
Cited alongside, same era.
V. Joshi, R. Zhao, R. R. Mehta, K. Kumar, and J. Li, “Transfer learning approaches for streaming end-to-end speech recognition system,” Proc. Interspeech , pp. 2152–2156, 2020
2020
Cited alongside, same era.
Google, “Artificial Intelligence at Google: Our Principles.” [Online]. Available: "https://ai.google/principles/"
Cited in the paper.
2021
Later among the works it cites.
A. Jayasimha and P. Paramasivam, “Personalizing speech start point and end point detection in ASR systems from speaker embeddings,” in 2021 IEEE Spoken Language Technology Workshop (SLT) . IEEE, 2021, pp. 771–777
2021
Later among the works it cites.
2021
Later among the works it cites.
B. Li, A. Gulati, J. Yu, T. N. Sainath, C.-C. Chiu, A. Narayanan, S.-Y. Chang, R. Pang, Y. He, J. Qin et al. , “A better and faster end-to-end model for streaming ASR,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 5634–5638
2021
Later among the works it cites.