SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1125 of 3012 papers

TitleStatusHype
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding—0
Comparison of Soft and Hard Target RNN-T Distillation for Large-scale ASR—0
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch—0
ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems—0
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR—0
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary—0
ASR error management for improving spoken language understanding—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
Acoustic Model Fusion for End-to-end Speech Recognition—0
Accent Recognition with Hybrid Phonetic Features—0
Comparing Two Basic Methods for Discriminating Between Similar Languages and Varieties—0
ASR Error Detection via Audio-Transcript entailment—0
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
Comparing Discrete and Continuous Space LLMs for Speech Recognition—0
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model—0
ASR Error Correction using Large Language Models—0
Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task—0
Comparative Analysis of the wav2vec 2.0 Feature Extractor—0
ASR Error Correction and Domain Adaptation Using Machine Translation—0
A Generative Model of a Pronunciation Lexicon for Hindi—0
Acoustic Model Compression with MAP adaptation—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction—0
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice—0
Show:102550
← PrevPage 45 of 121Next →
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