{"total":32,"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":"2303.03329","title":"End-to-End Speech Recognition: A Survey","created":"2023-03-03","cited_by_count":129,"area":null},{"id":"2102.01380","title":"Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition","created":"2021-02-02","cited_by_count":37,"area":null},{"id":"2109.07513","title":"Tied & Reduced RNN-T Decoder","created":"2021-09-15","cited_by_count":36,"area":null},{"id":"2212.04972","title":"MOPRD: A multidisciplinary open peer review dataset","created":"2022-12-09","cited_by_count":32,"area":null},{"id":"2104.14297","title":"End-to-End Speech Recognition from Federated Acoustic Models","created":"2021-04-29","cited_by_count":30,"area":null},{"id":"2205.01086","title":"Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages","created":"2022-05-02","cited_by_count":26,"area":null},{"id":"2109.14320","title":"Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks","created":"2021-09-29","cited_by_count":23,"area":null},{"id":"2210.16481","title":"Accelerating RNN-T Training and Inference Using CTC guidance","created":"2022-10-29","cited_by_count":17,"area":null},{"id":"2406.02166","title":"Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision","created":"2024-06-04","cited_by_count":17,"area":null},{"id":"2208.12666","title":"Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages","created":"2022-08-26","cited_by_count":16,"area":null},{"id":"2102.06474","title":"Transformer Language Models with LSTM-based Cross-utterance Information Representation","created":"2021-02-12","cited_by_count":15,"area":null},{"id":"2110.04109","title":"Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units","created":"2021-10-08","cited_by_count":13,"area":null},{"id":"2102.07370","title":"Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification","created":"2021-02-15","cited_by_count":11,"area":null},{"id":"2103.14736","title":"Construction of a Large-scale Japanese ASR Corpus on TV Recordings","created":"2021-03-26","cited_by_count":8,"area":null},{"id":"2110.04891","title":"Have best of both worlds: two-pass hybrid and E2E cascading framework for speech recognition","created":"2021-10-10","cited_by_count":7,"area":null},{"id":"2307.06530","title":"Exploring the Integration of Large Language Models into Automatic Speech Recognition Systems: An Empirical Study","created":"2023-07-13","cited_by_count":7,"area":null},{"id":"2209.15575","title":"Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio","created":"2022-09-30","cited_by_count":6,"area":null},{"id":"2201.11624","title":"LiteLSTM Architecture for Deep Recurrent Neural Networks","created":"2022-01-27","cited_by_count":5,"area":null},{"id":"2309.11400","title":"Transformers versus LSTMs for electronic trading","created":"2023-09-20","cited_by_count":4,"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":"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":"2111.02654","title":"Speech recognition for air traffic control via feature learning and end-to-end training","created":"2021-11-04","cited_by_count":2,"area":null},{"id":"2212.02135","title":"SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-Labels","created":"2022-12-05","cited_by_count":2,"area":null},{"id":"2304.01905","title":"Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition","created":"2023-04-03","cited_by_count":2,"area":null},{"id":"2210.11642","title":"Improving Semi-supervised End-to-end Automatic Speech Recognition using CycleGAN and Inter-domain Losses","created":"2022-10-20","cited_by_count":1,"area":null},{"id":"2301.04794","title":"LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks","created":"2023-01-12","cited_by_count":1,"area":null},{"id":"2404.18976","title":"Foundations of Multisensory Artificial Intelligence","created":"2024-04-29","cited_by_count":1,"area":null},{"id":"2407.13292","title":"Low-Resourced Speech Recognition for Iu Mien Language via Weakly-Supervised Phoneme-based Multilingual Pre-training","created":"2024-07-18","cited_by_count":1,"area":null},{"id":"2203.00328","title":"BERT-LID: Leveraging BERT to Improve Spoken Language Identification","created":"2022-03-01","cited_by_count":0,"area":null},{"id":"2305.18596","title":"Building Accurate Low Latency ASR for Streaming Voice Search","created":"2023-05-29","cited_by_count":0,"area":null},{"id":"2306.11473","title":"Timestamped Embedding-Matching Acoustic-to-Word CTC ASR","created":"2023-06-20","cited_by_count":0,"area":null}],"next_cursor":null}