{"total":13,"rows":[{"id":"2303.03329","title":"End-to-End Speech Recognition: A Survey","created":"2023-03-03","cited_by_count":129,"area":null},{"id":"2310.09424","title":"SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation","created":"2023-10-13","cited_by_count":76,"area":null},{"id":"2104.02882","title":"FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization","created":"2021-04-07","cited_by_count":11,"area":null},{"id":"2406.06220","title":"Label-Looping: Highly Efficient Decoding for Transducers","created":"2024-06-10","cited_by_count":9,"area":null},{"id":"2406.07096","title":"Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter","created":"2024-06-11","cited_by_count":7,"area":null},{"id":"2505.22857","title":"NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding","created":"2025-05-28","cited_by_count":7,"area":null},{"id":"2104.06104","title":"Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept","created":"2021-04-13","cited_by_count":5,"area":null},{"id":"2506.00185","title":"Pushing the Limits of Beam Search Decoding for Transducer-based ASR models","created":"2025-05-30","cited_by_count":4,"area":null},{"id":"2108.10752","title":"Generalizing RNN-Transducer to Out-Domain Audio via Sparse Self-Attention Layers","created":"2021-08-22","cited_by_count":2,"area":null},{"id":"2305.17304","title":"External Language Model Integration for Factorized Neural Transducers","created":"2023-05-26","cited_by_count":2,"area":null},{"id":"2507.18161","title":"Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges","created":"2025-07-24","cited_by_count":2,"area":null},{"id":"2505.13765","title":"WIND: Accelerated RNN-T Decoding with Windowed Inference for Non-blank Detection","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2309.08626","title":"Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method","created":"2023-09-12","cited_by_count":0,"area":null}],"next_cursor":null}