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Towards better performance with heterogeneous training data in acoustic modeling using deep neural networks.

, , , and . INTERSPEECH, page 845-849. ISCA, (2014)

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Environment normalization training and environment adaptation using mixture stochastic trajectory model., , and . Speech Commun., 26 (4): 245-258 (1998)Radio Frequency Fingerprinting on the Edge., , , , , , , , , and . IEEE Trans. Mob. Comput., 21 (11): 4078-4093 (2022)Switchable Wide-Band Multi-Wavelength Brillouin-Erbium Fiber Laser Based on Random Distributed Feedback., and . IEEE Access, (2020)A minimum cross-entropy approach to hidden Markov model adaptation., , and . IEEE Signal Process. Lett., 6 (6): 132-134 (1999)Have Best of Both Worlds: Two-Pass Hybrid and E2E Cascading Framework for Speech Recognition., , , and . ICASSP, page 7432-7436. IEEE, (2022)Nonlinear vectorial interpolation for speaker recognition., and . ICASSP, page 173-176. IEEE Computer Society, (1992)Internal Language Model Training for Domain-Adaptive End-To-End Speech Recognition., , , , , , , , and . ICASSP, page 7338-7342. IEEE, (2021)Hidden Markov Model Diarisation with Speaker Location Information., , and . ICASSP, page 7158-7162. IEEE, (2021)Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution., , , , , , , , , and . ECCV (19), volume 13679 of Lecture Notes in Computer Science, page 92-111. Springer, (2022)Rapid RNN-T Adaptation Using Personalized Speech Synthesis and Neural Language Generator., , , , , and . INTERSPEECH, page 1256-1260. ISCA, (2020)