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Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting.

M. Sun, A. Raju, G. Tucker, S. Panchapagesan, G. Fu, A. Mandal, S. Matsoukas, N. Strom, and S. Vitaladevuni. CoRR, (2017)

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Topic-based Evaluation for Conversational Bots.F. Guo, A. Metallinou, C. Khatri, A. Raju, A. Venkatesh, and A. Ram. CoRR, (2018)Time-Delayed Bottleneck Highway Networks Using a DFT Feature for Keyword Spotting.J. Guo, K. Kumatani, M. Sun, M. Wu, A. Raju, N. Strom, and A. Mandal. ICASSP, page 5489-5493. IEEE, (2018)Improving Noise Robustness of Automatic Speech Recognition via Parallel Data and Teacher-student Learning.L. Mosner, M. Wu, A. Raju, S. Parthasarathi, K. Kumatani, S. Sundaram, R. Maas, and B. Hoffmeister. ICASSP, page 6475-6479. IEEE, (2019)Scalable Multi Corpora Neural Language Models for ASR.A. Raju, D. Filimonov, G. Tiwari, G. Lan, and A. Rastrow. CoRR, (2019)Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting.M. Sun, A. Raju, G. Tucker, S. Panchapagesan, G. Fu, A. Mandal, S. Matsoukas, N. Strom, and S. Vitaladevuni. CoRR, (2017)Non-linear dimension reduction of Gabor features for noise-robust ASR.H. Gupta, A. Raju, and A. Alwan. ICASSP, page 1715-1719. IEEE, (2014)Data Augmentation for Robust Keyword Spotting under Playback Interference.A. Raju, S. Panchapagesan, X. Liu, A. Mandal, and N. Strom. CoRR, (2018)On Evaluating and Comparing Conversational Agents.A. Venkatesh, C. Khatri, A. Ram, F. Guo, R. Gabriel, A. Nagar, R. Prasad, M. Cheng, B. Hedayatnia, A. Metallinou and 3 other author(s). CoRR, (2018)Contextual Language Model Adaptation for Conversational Agents.A. Raju, B. Hedayatnia, L. Liu, A. Gandhe, C. Khatri, A. Metallinou, A. Venkatesh, and A. Rastrow. INTERSPEECH, page 3333-3337. ISCA, (2018)Multi-Task Language Modeling for Improving Speech Recognition of Rare Words.C. Yang, L. Liu, A. Gandhe, Y. Gu, A. Raju, D. Filimonov, and I. Bulyko. ASRU, page 1087-1093. IEEE, (2021)

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Disambiguation of "Raju, Anirudh"

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Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting.

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Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting.