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Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR.

P. Dighe, P. Nayak, O. Rudovic, E. Marchi, X. Niu, and A. Tewfik. CoRR, (2022)

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Low-rank and sparse soft targets to learn better DNN acoustic models.P. Dighe, A. Asaei, and H. Bourlard. ICASSP, page 5265-5269. IEEE, (2017)Far-Field ASR Using Low-Rank and Sparse Soft Targets from Parallel Data.P. Dighe, A. Asaei, and H. Bourlard. SLT, page 581-587. IEEE, (2018)Streaming on-device detection of device directed speech from voice and touch-based invocation.O. Rudovic, A. Bindal, V. Garg, P. Simha, P. Dighe, and S. Kajarekar. CoRR, (2021)Scale independent raga identification using chromagram patterns and swara based features.P. Dighe, P. Agarwal, H. Karnick, S. Thota, and B. Raj. ICME Workshops, page 1-4. IEEE Computer Society, (2013)Analyzing Uncertainties in Speech Recognition Using Dropout.A. Vyas, P. Dighe, S. Tong, and H. Bourlard. ICASSP, page 6730-6734. IEEE, (2019)Language identification using spectro-temporal patch features.K. Sahni, P. Dighe, R. Singh, and B. Raj. SAPA@INTERSPEECH, page 110-113. ISCA, (2012)Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models.V. Garg, O. Rudovic, P. Dighe, A. Abdelaziz, E. Marchi, S. Adya, C. Dhir, and A. Tewfik. INTERSPEECH, page 1258-1262. ISCA, (2022)Detecting and labeling speakers on overlapping speech using vector taylor series.P. Dighe, M. Ferras, and H. Bourlard. INTERSPEECH, page 592-596. ISCA, (2014)Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR.P. Dighe, P. Nayak, O. Rudovic, E. Marchi, X. Niu, and A. Tewfik. ICASSP, page 1-5. IEEE, (2023)Less Is More: A Unified Architecture for Device-Directed Speech Detection with Multiple Invocation Types.O. Rudovic, W. Chang, V. Garg, P. Dighe, P. Simha, J. Berkowitz, A. Abdelaziz, S. Kajarekar, E. Marchi, and S. Adya. ICASSP, page 1-5. IEEE, (2023)

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Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR.

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Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR.