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 1701–1725 of 3012 papers

TitleStatusHype
On Prosody Modeling for ASR+TTS based Voice Conversion—0
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation—0
Token-Level Supervised Contrastive Learning for Punctuation RestorationCode1
A baseline model for computationally inexpensive speech recognition for Kazakh using the Coqui STT framework—0
STRODE: Stochastic Boundary Ordinary Differential EquationCode1
A Comparison of Methods for OOV-word Recognition on a New Public DatasetCode1
VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording—0
Multi-task Learning with Cross Attention for Keyword Spotting—0
Zero-shot Speech Translation—0
A Configurable Multilingual Model is All You Need to Recognize All Languages—0
The IWSLT 2021 BUT Speech Translation Systems—0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems—0
Layer-wise Analysis of a Self-supervised Speech Representation ModelCode1
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition—0
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models—0
Noisy Training Improves E2E ASR for the Edge—0
Improved Language Identification Through Cross-Lingual Self-Supervised Learning—0
End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning—0
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers—0
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio—0
Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech RecognitionCode0
Investigation of Practical Aspects of Single Channel Speech Separation for ASR—0
Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition—0
Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation—0
TENET: A Time-reversal Enhancement Network for Noise-robust ASRCode1
Show:102550
← PrevPage 69 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