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A novel method of artificial bandwidth extension using deep architecture.

, , , , and . INTERSPEECH, page 2598-2602. ISCA, (2015)

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TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition., , , , , , and . CoRR, (2021)Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning., , , , , and . CoRR, (2020)Distilling Knowledge Using Parallel Data for Far-field Speech Recognition., , , and . CoRR, (2018)Decoupling Pronunciation and Language for End-to-End Code-Switching Automatic Speech Recognition., , , , , and . ICASSP, page 6249-6253. IEEE, (2021)Fast End-to-End Speech Recognition Via Non-Autoregressive Models and Cross-Modal Knowledge Transferring From BERT., , , , , and . IEEE ACM Trans. Audio Speech Lang. Process., (2021)VLP2MSA: Expanding vision-language pre-training to multimodal sentiment analysis., , , , , , , , and . Knowl. Based Syst., (January 2024)Improving Deep Neural Network Based Speech Synthesis through Contextual Feature Parametrization and Multi-Task Learning., , , , and . J. Signal Process. Syst., 90 (7): 1025-1037 (2018)Deep Metric Learning for the Target Cost in Unit-Selection Speech Synthesizer., , , and . INTERSPEECH, page 2514-2518. ISCA, (2018)On the Application and Compression of Deep Time Delay Neural Network for Embedded Statistical Parametric Speech Synthesis., , , and . INTERSPEECH, page 922-926. ISCA, (2018)Pitch-Scaled Analysis based Residual Reconstruction for Speech Analysis and Synthesis., , and . INTERSPEECH, page 374-377. ISCA, (2012)