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Automatic Speech Recognition (ASR)

Automatic Speech Recognition (ASR) involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.

Papers

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Showing 1751–1800 of 3012 papers

TitleStatusHype
Transformer ASR with Contextual Block Processing—0
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation—0
Transformer-based Automatic Speech Recognition of Formal and Colloquial Czech in MALACH Project—0
Transformer-based Model for ASR N-Best Rescoring and Rewriting—0
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture—0
Transformer-based Streaming ASR with Cumulative Attention—0
Transformer-Based Video Front-Ends for Audio-Visual Speech Recognition for Single and Multi-Person Video—0
Transformer-Transducers for Code-Switched Speech Recognition—0
Transformer with Bidirectional Decoder for Speech Recognition—0
Transforming NLU with Babylon: A Case Study in Development of Real-time, Edge-Efficient, Multi-Intent Translation System for Automated Drive-Thru Ordering—0
Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base—0
TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognition—0
Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition—0
Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition—0
Tropical Modeling of Weighted Transducer Algorithms on Graphs—0
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection—0
t-SOT FNT: Streaming Multi-talker ASR with Text-only Domain Adaptation Capability—0
TTS Skins: Speaker Conversion via ASR—0
TUKE-BNews-SK: Slovak Broadcast News Corpus Construction and Evaluation—0
Tutorial Proposal: End-to-End Speech Translation—0
Two Front-Ends, One Model : Fusing Heterogeneous Speech Features for Low Resource ASR with Multilingual Pre-Training—0
Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems—0
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR—0
Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews—0
A Multi-level Acoustic Feature Extraction Framework for Transformer Based End-to-End Speech Recognition—0
U2++ MoE: Scaling 4.7x parameters with minimal impact on RTF—0
UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction—0
UFO2: A unified pre-training framework for online and offline speech recognition—0
UME: Upcycling Mixture-of-Experts for Scalable and Efficient Automatic Speech Recognition—0
UML: A Universal Monolingual Output Layer for Multilingual ASR—0
Understanding Semantics from Speech Through Pre-training—0
Understanding Shared Speech-Text Representations—0
Understanding the Role of Self Attention for Efficient Speech Recognition—0
Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration—0
Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation—0
Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems—0
Unified Modeling of Multi-Domain Multi-Device ASR Systems—0
Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator—0
Unifying Streaming and Non-streaming Zipformer-based ASR—0
Unintended Memorization in Large ASR Models, and How to Mitigate It—0
Universal-2-TF: Robust All-Neural Text Formatting for ASR—0
Universal Adversarial Perturbations for Speech Recognition Systems—0
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling—0
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions—0
Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition—0
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition—0
Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition—0
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition—0
Unsupervised and Efficient Vocabulary Expansion for Recurrent Neural Network Language Models in ASR—0
Unsupervised ASR via Cross-Lingual Pseudo-Labeling—0
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All datasetsLRS2RealMANSagaleeHUI speech corpusLRS3-TEDM-AILabs speech datasetThe Spoken Wikipedia CorporaVoxforge GermanVoxPopuli

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1—Unverified
2TM-seq2seqTest WER9.7—Unverified
3CTC/attentionTest WER8.2—Unverified
4LF-MMI TDNNTest WER6.7—Unverified
5Whisper-LLaMATest WER6.6—Unverified
6End2end ConformerTest WER3.9—Unverified
7End2end ConformerTest WER3.7—Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7—Unverified
9CTC/AttentionTest WER1.5—Unverified
10WhisperTest WER1.3—Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5—Unverified
2CleanMel-L-maskCER14.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32—Unverified
2Whisper-largev3-finetunedTest WER10.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89—Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98—Unverified