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Automatic Speech Recognition

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Showing 1501–1525 of 3174 papers

TitleStatusHype
Improving Data Driven Inverse Text Normalization using Data Augmentation—0
Improving Distinction between ASR Errors and Speech Disfluencies with Feature Space Interpolation—0
Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions—0
Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training—0
Improving EEG based Continuous Speech Recognition—0
Improving End-to-End Bangla Speech Recognition with Semi-supervised Training—0
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis—0
Improving Fairness and Robustness in End-to-End Speech Recognition through unsupervised clustering—0
Improving Fast-slow Encoder based Transducer with Streaming Deliberation—0
Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition—0
End-to-End Speech Translation of Arabic to English Broadcast News—0
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model—0
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech—0
Improving Language Model Adaptation using Automatic Data Selection and Neural Network—0
Improving Language Model Integration for Neural Machine Translation—0
Improving Large-scale Deep Biasing with Phoneme Features and Text-only Data in Streaming Transducer—0
Improving low-resource ASR performance with untranscribed out-of-domain data—0
Improving Low Resource Code-switched ASR using Augmented Code-switched TTS—0
Bilingual End-to-End ASR with Byte-Level Subwords—0
Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training—0
An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition—0
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition—0
End-to-End Speech-to-Text Translation: A Survey—0
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model—0
End-to-end Speech-to-Punctuated-Text Recognition—0
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