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On Temporal Context Information for Hybrid BLSTM-Based Phoneme Recognition.

, , and . ASRU, page 516-523. IEEE, (2019)

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On Temporal Context Information for Hybrid BLSTM-Based Phoneme Recognition., , and . ASRU, page 516-523. IEEE, (2019)Fully Convolutional Recurrent Networks for Speech Enhancement., , , , and . ICASSP, page 6674-6678. IEEE, (2020)Artificial bandwidth extension using deep neural networks for spectral envelope estimation., , and . IWAENC, page 1-5. IEEE, (2016)Self-Attention With Restricted Time Context And Resolution In Dnn Speech Enhancement., , and . IWAENC, page 1-5. IEEE, (2022)Does a PESQNet (Loss) Require a Clean Reference Input? The Original PESQ Does, But ACR Listening Tests Don't., , and . IWAENC, page 1-5. IEEE, (2022)A Simple Cepstral Domain DNN Approach to Artificial Speech Bandwidth Extension., , and . ICASSP, page 5469-5473. IEEE, (2018)Deep Noise Suppression with Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data., , and . Interspeech, page 2806-2810. ISCA, (2021)Y2-Net FCRN for Acoustic Echo and Noise Suppression., , , and . Interspeech, page 4763-4767. ISCA, (2021)Separated Noise Suppression and Speech Restoration: Lstm-Based Speech Enhancement in Two Stages., , , , and . WASPAA, page 239-243. IEEE, (2019)Concatenated Identical DNN (CI-DNN) to Reduce Noise-Type Dependence in DNN-Based Speech Enhancement., , and . EUSIPCO, page 1-5. IEEE, (2019)