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Automatic Speech Recognition

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Showing 1526–1550 of 3174 papers

TitleStatusHype
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking—0
Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC—0
Improving Multimodal Speech Recognition by Data Augmentation and Speech Representations—0
Improving Named Entity Recognition in Spoken Dialog Systems by Context and Speech Pattern Modeling—0
Improving Named Entity Transcription with Contextual LLM-based Revision—0
Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation—0
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network—0
Improving Noise Robustness of an End-to-End Neural Model for Automatic Speech Recognition—0
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning—0
Bilingual End-to-End ASR with Byte-Level Subwords—0
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition—0
Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models—0
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios—0
An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition—0
Improving Punctuation Restoration for Speech Transcripts via External Data—0
Improving Rare Words Recognition through Homophone Extension and Unified Writing for Low-resource Cantonese Speech Recognition—0
Improving Readability for Automatic Speech Recognition Transcription—0
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition—0
Improving RNN-T ASR Performance with Date-Time and Location Awareness—0
End-to-End Speech-to-Text Translation: A Survey—0
Contrastive Semi-supervised Learning for ASR—0
Improving RNN transducer with normalized jointer network—0
Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method—0
Improving Scheduled Sampling for Neural Transducer-based ASR—0
End-to-end Speech-to-Punctuated-Text Recognition—0
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