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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 1851–1900 of 3012 papers

TitleStatusHype
V-Cloak: Intelligibility-, Naturalness- & Timbre-Preserving Real-Time Voice Anonymization—0
VietASR: Achieving Industry-level Vietnamese ASR with 50-hour labeled data and Large-Scale Speech Pretraining—0
VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition—0
ViraPart: A Text Refinement Framework for Automatic Speech Recognition and Natural Language Processing Tasks in Persian—0
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech—0
Visual-Aware Speech Recognition for Noisy Scenarios—0
Visual Information Matters for ASR Error Correction—0
Visualizing Automatic Speech Recognition -- Means for a Better Understanding?—0
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer—0
Voice Privacy with Smart Digital Assistants in Educational Settings—0
Voice Quality and Pitch Features in Transformer-Based Speech Recognition—0
Voice Query Auto Completion—0
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System—0
VoxHakka: A Dialectally Diverse Multi-speaker Text-to-Speech System for Taiwanese Hakka—0
VQ-CTAP: Cross-Modal Fine-Grained Sequence Representation Learning for Speech Processing—0
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment—0
Warped Language Models for Noise Robust Language Understanding—0
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR—0
Wav2Prompt: End-to-End Speech Prompt Generation and Tuning For LLM in Zero and Few-shot Learning—0
wav2vec and its current potential to Automatic Speech Recognition in German for the usage in Digital History: A comparative assessment of available ASR-technologies for the use in cultural heritage contexts—0
Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR—0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
W-CTC: a Connectionist Temporal Classification Loss with Wild Cards—0
Weak-Attention Suppression For Transformer Based Speech Recognition—0
Weakly Supervised Construction of ASR Systems with Massive Video Data—0
Weight Averaging: A Simple Yet Effective Method to Overcome Catastrophic Forgetting in Automatic Speech Recognition—0
Weighted-Sampling Audio Adversarial Example Attack—0
WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm—0
WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition—0
WER we are and WER we think we are—0
WER We Stand: Benchmarking Urdu ASR Models—0
WEST: Word Encoded Sequence Transducers—0
What is lost in Normalization? Exploring Pitfalls in Multilingual ASR Model Evaluations—0
When CTC Training Meets Acoustic Landmarks—0
When End-to-End is Overkill: Rethinking Cascaded Speech-to-Text Translation—0
Where are we in Named Entity Recognition from Speech?—0
Where are we in semantic concept extraction for Spoken Language Understanding?—0
Which French speech recognition system for assistant robots?—0
Whisper Finetuning on Nepali Language—0
Whispering in Amharic: Fine-tuning Whisper for Low-resource Language—0
Whispering in Norwegian: Navigating Orthographic and Dialectic Challenges—0
WhisperKit: On-device Real-time ASR with Billion-Scale Transformers—0
Whither the Priors for (Vocal) Interactivity?—0
Who Are We Talking About? Handling Person Names in Speech Translation—0
Who Are We Talking About? Handling Person Names in Speech Translation—0
Who Needs Decoders? Efficient Estimation of Sequence-level Attributes—0
Why Does Decentralized Training Outperform Synchronous Training In The Large Batch Setting?—0
Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR Customization—0
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 2021—0
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition—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