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 1651–1675 of 3012 papers

TitleStatusHype
Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection—0
NUVA: A Naming Utterance Verifier for Aphasia Treatment—0
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022—0
OAVA: the open audio-visual archives aggregator—0
Off-topic Response Detection for Spontaneous Spoken English Assessment—0
OLR 2021 Challenge: Datasets, Rules and Baselines—0
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet—0
On Building Spoken Language Understanding Systems for Low Resourced Languages—0
On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Mode—0
On Construction of the ASR-oriented Indian English Pronunciation Dictionary—0
On-Device detection of sentence completion for voice assistants with low-memory footprint—0
On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech—0
On-Device Speaker Anonymization of Acoustic Embeddings for ASR based onFlexible Location Gradient Reversal Layer—0
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement—0
One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition—0
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers—0
One-To-Many Multilingual End-to-end Speech Translation—0
On Knowledge Distillation for Direct Speech Translation—0
On Knowledge Distillation for Translating Erroneous Speech Transcriptions—0
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models—0
Online Automatic Speech Recognition with Listen, Attend and Spell Model—0
Online Continual Learning of End-to-End Speech Recognition Models—0
Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture—0
On Modular Training of Neural Acoustics-to-Word Model for LVCSR—0
On Neural Phone Recognition of Mixed-Source ECoG Signals—0
Show:102550
← PrevPage 67 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