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Extractive speech summarization leveraging convolutional neural network techniques., , , and . SLT, page 158-164. IEEE, (2016)Leveraging relevance cues for language modeling in speech recognition., and . Inf. Process. Manag., 49 (4): 807-816 (2013)On the Range Maximum-Sum Segment Query Problem., and . ISAAC, volume 3341 of Lecture Notes in Computer Science, page 294-305. Springer, (2004)Query modeling for spoken document retrieval., , and . ASRU, page 389-394. IEEE, (2011)A 0.31-pJ/bit 20-Gb/s DFE With 1 Discrete Tap and 2 IIR Filters Feedback in 40-nm-LP CMOS., , and . IEEE Trans. Circuits Syst. II Express Briefs, 64-II (11): 1282-1286 (2017)Toward Zero-Shot and Zero-Resource Multilingual Question Answering., and . IEEE Access, (2022)Completely Unsupervised Phoneme Recognition by a Generative Adversarial Network Harmonized with Iteratively Refined Hidden Markov Models., , , , and . INTERSPEECH, page 1856-1860. ISCA, (2019)Leveraging Relevance Cues for Improved Spoken Document Retrieval., , and . INTERSPEECH, page 929-932. ISCA, (2011)Exploring the Encoder Layers of Discriminative Autoencoders for LVCSR., , , , , and . INTERSPEECH, page 1631-1635. ISCA, (2019)GALs: 基於對抗式學習之整列式摘要法 (GALs: A GAN-based Listwise Summarizer)., and . ROCLING, page 15-24. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), (2019)