{"total":23,"rows":[{"id":"2106.06909","title":"GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio","created":"2021-06-13","cited_by_count":399,"area":null},{"id":"2111.01690","title":"Recent Advances in End-to-End Automatic Speech Recognition","created":"2021-11-02","cited_by_count":140,"area":null},{"id":"2303.03329","title":"End-to-End Speech Recognition: A Survey","created":"2023-03-03","cited_by_count":129,"area":null},{"id":"2103.09935","title":"Advancing RNN Transducer Technology for Speech Recognition","created":"2021-03-17","cited_by_count":44,"area":null},{"id":"2109.07513","title":"Tied & Reduced RNN-T Decoder","created":"2021-09-15","cited_by_count":36,"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":"2110.05354","title":"Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition","created":"2021-10-06","cited_by_count":17,"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":"2104.04552","title":"Lookup-Table Recurrent Language Models for Long Tail Speech Recognition","created":"2021-04-09","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":"2106.09760","title":"Multi-mode Transformer Transducer with Stochastic Future Context","created":"2021-06-17","cited_by_count":7,"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":"2110.09245","title":"Efficient Sequence Training of Attention Models using Approximative Recombination","created":"2021-10-18","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":"2106.04275","title":"Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition","created":"2021-06-08","cited_by_count":2,"area":null},{"id":"2211.06478","title":"Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting","created":"2022-11-11","cited_by_count":2,"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":"2307.10975","title":"Globally Normalising the Transducer for Streaming Speech Recognition","created":"2023-07-20","cited_by_count":0,"area":null}],"next_cursor":null}