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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 1076–1100 of 3012 papers

TitleStatusHype
Assessing the Performance of Automatic Speech Recognition Systems When Used by Native and Non-Native Speakers of Three Major Languages in Dictation Workflows—0
Configurable Privacy-Preserving Automatic Speech Recognition—0
Assessing ASR Model Quality on Disordered Speech using BERTScore—0
A higher order Minkowski loss for improved prediction ability of acoustic model in ASR—0
Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation—0
Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings—0
LegoSLM: Connecting LLM with Speech Encoder using CTC Posteriors—0
Towards interfacing large language models with ASR systems using confidence measures and prompting—0
Confidence Score Based Conformer Speaker Adaptation for Speech Recognition—0
ASR Rescoring and Confidence Estimation with ELECTRA—0
Conditioning Sequence-to-sequence Networks with Learned Activations—0
ASR is all you need: cross-modal distillation for lip reading—0
A Hierarchical Reasoning Graph Neural Network for The Automatic Scoring of Answer Transcriptions in Video Job Interviews—0
Concept-Based Embeddings for Natural Language Processing—0
ASR in German: A Detailed Error Analysis—0
Computing Optimal Location of Microphone for Improved Speech Recognition—0
Compute Cost Amortized Transformer for Streaming ASR—0
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding—0
A Hierarchical Neural Model for Learning Sequences of Dialogue Acts—0
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition—0
Comprehensive Punctuation Restoration for English and Polish—0
Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding—0
ASR for Non-standardised Languages with Dialectal Variation: the case of Swiss German—0
Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation—0
ASR for Documenting Acutely Under-Resourced Indigenous Languages—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