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Exploring GMM-derived Features for Unsupervised Adaptation of Deep Neural Network Acoustic Models.

, , , and . SPECOM, volume 9811 of Lecture Notes in Computer Science, page 304-311. Springer, (2016)

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Recent Advances in End-to-End Spoken Language Understanding., , , , and . SLSP, volume 11816 of Lecture Notes in Computer Science, page 44-55. Springer, (2019)LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech., , , , , , , , , and 12 other author(s). CoRR, (2023)An Investigation of Mixup Training Strategies for Acoustic Models in ASR., , , , , , and . INTERSPEECH, page 2903-2907. ISCA, (2018)GMM-derived features for effective unsupervised adaptation of deep neural network acoustic models., and . INTERSPEECH, page 2882-2886. ISCA, (2015)Speaker Adaptive Training and Mixup Regularization for Neural Network Acoustic Models in Automatic Speech Recognition., , and . INTERSPEECH, page 2414-2418. ISCA, (2018)Investigating Self-Supervised Pre-Training for End-to-End Speech Translation., , , , and . INTERSPEECH, page 1466-1470. ISCA, (2020)Speaking Rate Estimation Based on Deep Neural Networks., and . SPECOM, volume 8773 of Lecture Notes in Computer Science, page 418-424. Springer, (2014)Speaker Verification Using Spectral and Durational Segmental Characteristics., , , and . SPECOM, volume 9319 of Lecture Notes in Computer Science, page 397-404. Springer, (2015)The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment., , , , , , , and . INTERSPEECH, page 1698-1702. ISCA, (2020)A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation., , and . SLSP, volume 9918 of Lecture Notes in Computer Science, page 120-132. (2016)