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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. ICASSP, page 1-5. IEEE, (2023)

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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)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)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)Knowledge Transfer for Efficient on-Device False Trigger Mitigation.P. Dighe, E. Marchi, S. Vishnubhotla, S. Kajarekar, and D. Naik. ICASSP, page 6838-6842. IEEE, (2021)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)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)

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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.