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Improving ASR Robustness to Perturbed Speech Using Cycle-consistent Generative Adversarial Networks.

, , , and . ICASSP, page 5726-5730. IEEE, (2019)

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Improving ASR Robustness to Perturbed Speech Using Cycle-consistent Generative Adversarial Networks., , , and . ICASSP, page 5726-5730. IEEE, (2019)Learning to Model Prosodic and Spectral Features for Non-parallel Emotive Speech Conversion., and . Canadian AI, Canadian Artificial Intelligence Association, (2021)An Algorithm for Detection of Breath Sounds in Spontaneous Speech with Application to Speaker Recognition., and . SPECOM, volume 10458 of Lecture Notes in Computer Science, page 98-108. Springer, (2017)k-FFNN: A priori knowledge infused Feed-forward Neural Networks., , and . CoRR, (2017)Knowledge-Driven Feed-Forward Neural Network for Audio Affective Content Analysis., , and . AAAI Workshops, volume WS-18 of AAAI Technical Report, page 16-20. AAAI Press, (2018)Sentiment Classification on Erroneous ASR Transcripts: A Multi View Learning Approach., , , and . SLT, page 807-814. IEEE, (2018)On Combining Global and Localized Self-Supervised Models of Speech., , , and . INTERSPEECH, page 3593-3597. ISCA, (2022)Improved speaker recognition system for stressed speech using deep neural networks., and . IJCNN, page 1257-1264. IEEE, (2017)Analysis of laughter and speech-laugh signals using excitation source information., , , and . ICASSP, page 975-979. IEEE, (2014)Estimating Severity of Depression From Acoustic Features and Embeddings of Natural Speech., , , , , and . ICASSP, page 7278-7282. IEEE, (2021)