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Improved Robustness to Disfluencies in Rnn-Transducer Based Speech Recognition.

, , , and . ICASSP, page 6878-6882. IEEE, (2021)

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End-To-End Training of a Neural HMM with Label and Transition Probabilities., , , , and . ASRU, page 1-8. IEEE, (2023)Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition., , and . ICASSP, page 2661-2665. IEEE, (2018)Improving Factored Hybrid HMM Acoustic Modeling without State Tying., , , and . ICASSP, page 7442-7446. IEEE, (2022)Improved Robustness to Disfluencies in Rnn-Transducer Based Speech Recognition., , , and . ICASSP, page 6878-6882. IEEE, (2021)HMM vs. CTC for Automatic Speech Recognition: Comparison Based on Full-Sum Training from Scratch., , , , and . SLT, page 287-294. IEEE, (2022)Improved Robustness to Disfluencies in RNN-Transducer Based Speech Recognition., , , and . CoRR, (2020)Sample drop detection for asynchronous devices distributed in space., , and . EUSIPCO, page 815-819. IEEE, (2020)Context-Dependent Acoustic Modeling Without Explicit Phone Clustering., , , and . INTERSPEECH, page 4377-4381. ISCA, (2020)Competitive and Resource Efficient Factored Hybrid HMM Systems are Simpler Than You Think., , , , and . INTERSPEECH, page 4938-4942. ISCA, (2023)Investigating the Effect of Label Topology and Training Criterion on ASR Performance and Alignment Quality., , , , and . CoRR, (2024)