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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 1651–1675 of 3012 papers

TitleStatusHype
The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models—0
The State of Commercial Automatic French Legal Speech Recognition Systems and their Impact on Court Reporters et al—0
The SUMMA Platform Prototype—0
The THUEE System Description for the IARPA OpenASR21 Challenge—0
The USFD Spoken Language Translation System for IWSLT 2014—0
The WaveSurfer Automatic Speech Recognition Plugin—0
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic—0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022—0
The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge—0
The ZevoMOS entry to VoiceMOS Challenge 2022—0
"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)—0
Thoughts on the potential to compensate a hearing loss in noise—0
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model—0
Thutmose Tagger: Single-pass neural model for Inverse Text Normalization—0
Tigrinya Automatic Speech recognition with Morpheme based recognition units—0
Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection—0
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification—0
Time-Domain Speech Enhancement for Robust Automatic Speech Recognition—0
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge—0
TLT-school: a Corpus of Non Native Children Speech—0
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models—0
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion—0
Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments—0
TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition—0
Topic Classification on Spoken Documents Using Deep Acoustic and Linguistic Features—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