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End-to-end spoken language understanding using transformer networks and self-supervised pre-trained features., , , , and . CoRR, (2020)Improving End-to-end Models for Set Prediction in Spoken Language Understanding., , , , and . ICASSP, page 7162-7166. IEEE, (2022)Towards Reducing the Need for Speech Training Data to Build Spoken Language Understanding Systems., , , and . ICASSP, page 7932-7936. IEEE, (2022)Resource-efficient TDNN Architectures for Audio-visual Speech Recognition., , , and . EUSIPCO, page 506-510. IEEE, (2021)Annealed dropout training of deep networks., , and . SLT, page 159-164. IEEE, (2014)Subspace Gaussian Mixture Models for speech recognition., , , , , , , , , and 3 other author(s). ICASSP, page 4330-4333. IEEE, (2010)A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition., , , , , , , , , and 17 other author(s). ICASSP, page 8111-8115. IEEE, (2013)Pre-training of Speaker Embeddings for Low-latency Speaker Change Detection in Broadcast News., , , and . ICASSP, page 6286-6290. IEEE, (2019)Hilbert Envelope Based Features for Far-Field Speech Recognition., , and . MLMI, volume 5237 of Lecture Notes in Computer Science, page 119-124. Springer, (2008)A Recorded Debating Dataset., , , , , , , , and . LREC, European Language Resources Association (ELRA), (2018)