{"total":34,"rows":[{"id":"2111.01690","title":"Recent Advances in End-to-End Automatic Speech Recognition","created":"2021-11-02","cited_by_count":140,"area":null},{"id":"2109.07513","title":"Tied & Reduced RNN-T Decoder","created":"2021-09-15","cited_by_count":36,"area":null},{"id":"2102.07739","title":"Personalization Strategies for End-to-End Speech Recognition Systems","created":"2021-02-15","cited_by_count":26,"area":null},{"id":"2103.08393","title":"Wav2vec-C: A Self-supervised Model for Speech Representation Learning","created":"2021-03-09","cited_by_count":25,"area":null},{"id":"2210.07353","title":"JOIST: A Joint Speech and Text Streaming Model For ASR","created":"2022-10-13","cited_by_count":24,"area":null},{"id":"2106.02302","title":"Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition","created":"2021-06-04","cited_by_count":19,"area":null},{"id":"2204.10586","title":"Efficient Training of Neural Transducer for Speech Recognition","created":"2022-04-22","cited_by_count":14,"area":null},{"id":"2204.07553","title":"Improving Rare Word Recognition with LM-aware MWER Training","created":"2022-04-15","cited_by_count":12,"area":null},{"id":"2205.09058","title":"Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator","created":"2022-05-18","cited_by_count":11,"area":null},{"id":"2110.03841","title":"Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognition","created":"2021-10-08","cited_by_count":10,"area":null},{"id":"2102.06750","title":"Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding","created":"2021-02-12","cited_by_count":9,"area":null},{"id":"2103.14152","title":"Residual Energy-Based Models for End-to-End Speech Recognition","created":"2021-03-25","cited_by_count":9,"area":null},{"id":"2112.02498","title":"Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI","created":"2021-12-05","cited_by_count":8,"area":null},{"id":"2204.06260","title":"Self-critical Sequence Training for Automatic Speech Recognition","created":"2022-04-13","cited_by_count":8,"area":null},{"id":"2309.15467","title":"Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey","created":"2023-09-27","cited_by_count":8,"area":null},{"id":"2303.10384","title":"Powerful and Extensible WFST Framework for RNN-Transducer Losses","created":"2023-03-18","cited_by_count":7,"area":null},{"id":"2106.15919","title":"On joint training with interfaces for spoken language understanding","created":"2021-06-30","cited_by_count":5,"area":null},{"id":"2201.03655","title":"A Likelihood Ratio based Domain Adaptation Method for E2E Models","created":"2022-01-10","cited_by_count":5,"area":null},{"id":"2412.16464","title":"Transducer-Llama: Integrating LLMs into Streamable Transducer-based Speech Recognition","created":"2024-12-21","cited_by_count":4,"area":null},{"id":"2104.02232","title":"Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios","created":"2021-04-06","cited_by_count":3,"area":null},{"id":"2203.15614","title":"Integrating Lattice-Free MMI into End-to-End Speech Recognition","created":"2022-03-29","cited_by_count":3,"area":null},{"id":"2203.16868","title":"Memory-Efficient Training of RNN-Transducer with Sampled Softmax","created":"2022-03-31","cited_by_count":3,"area":null},{"id":"2211.17094","title":"Better Transcription of UK Supreme Court Hearings","created":"2022-11-29","cited_by_count":3,"area":null},{"id":"2212.04325","title":"Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers","created":"2022-12-07","cited_by_count":3,"area":null},{"id":"2404.01716","title":"Effective internal language model training and fusion for factorized transducer model","created":"2024-04-02","cited_by_count":3,"area":null},{"id":"2407.16664","title":"Towards scalable efficient on-device ASR with transfer learning","created":"2024-07-23","cited_by_count":2,"area":null},{"id":"2207.14736","title":"Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducer","created":"2022-07-29","cited_by_count":1,"area":null},{"id":"2309.14130","title":"On the Relation between Internal Language Model and Sequence Discriminative Training for Neural Transducers","created":"2023-09-25","cited_by_count":1,"area":null},{"id":"2110.09324","title":"Automatic Learning of Subword Dependent Model Scales","created":"2021-10-18","cited_by_count":0,"area":null},{"id":"2306.06246","title":"Record Deduplication for Entity Distribution Modeling in ASR Transcripts","created":"2023-06-09","cited_by_count":0,"area":null},{"id":"2307.10975","title":"Globally Normalising the Transducer for Streaming Speech Recognition","created":"2023-07-20","cited_by_count":0,"area":null},{"id":"2308.07486","title":"O-1: Self-training with Oracle and 1-best Hypothesis","created":"2023-08-14","cited_by_count":0,"area":null},{"id":"2310.07345","title":"Investigating the Effect of Language Models in Sequence Discriminative Training for Neural Transducers","created":"2023-10-11","cited_by_count":0,"area":null},{"id":"2606.06985","title":"Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognition","created":"2026-06-05","cited_by_count":0,"area":null}],"next_cursor":null}