SOTAVerified

Automatic Speech Recognition

Papers

Showing 5175 of 3174 papers

TitleStatusHype
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPTCode2
Towards A Unified Conformer Structure: from ASR to ASV TaskCode2
LiteASR: Efficient Automatic Speech Recognition with Low-Rank ApproximationCode2
NusaCrowd: Open Source Initiative for Indonesian NLP ResourcesCode2
Auto-AVSR: Audio-Visual Speech Recognition with Automatic LabelsCode2
Fast Transformers with Clustered AttentionCode2
Learning Audio-Visual Speech Representation by Masked Multimodal Cluster PredictionCode2
DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech RecognitionCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface ElectromyographyCode2
Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile InstructionsCode2
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control CommunicationsCode1
A Systematic Comparison of Phonetic Aware Techniques for Speech EnhancementCode1
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy SpeechCode1
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and EnglishCode1
Framework for Curating Speech Datasets and Evaluating ASR Systems: A Case Study for PolishCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
Continuous speech separation: dataset and analysisCode1
ASR Error Correction with Constrained Decoding on Operation PredictionCode1
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMICode1
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversionCode1
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker OneCode1
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech RecognitionCode1
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global ContextCode1
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation ModelsCode1
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