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 1901–1925 of 3012 papers

TitleStatusHype
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
Word-level confidence estimation for RNN transducers—0
Word Order Does Not Matter For Speech Recognition—0
Words Worth: Verbal Content and Hirability Impressions in YouTube Video Resumes—0
Word Transduction for Addressing the OOV Problem in Machine Translation for Similar Resource-Scarce Languages—0
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese—0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation—0
You don't understand me!: Comparing ASR results for L1 and L2 speakers of Swedish—0
Your voice is your voice: Supporting Self-expression through Speech Generation and LLMs in Augmented and Alternative Communication—0
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus—0
Zero-resource Speech Translation and Recognition with LLMs—0
Zero-Shot Automatic Pronunciation Assessment—0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition—0
Zero-shot Disfluency Detection for Indian Languages—0
Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens—0
Zero-shot Speech Translation—0
Zero Shot Text to Speech Augmentation for Automatic Speech Recognition on Low-Resource Accented Speech Corpora—0
Zipformer: A faster and better encoder for automatic speech recognition—0
Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities—0
100,000 Podcasts: A Spoken English Document Corpus—0
L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition—0
Label-Synchronous Neural Transducer for Adaptable Online E2E Speech Recognition—0
Label-Synchronous Speech-to-Text Alignment for ASR Using Forward and Backward Transformers—0
Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks—0
LAMASSU: Streaming Language-Agnostic Multilingual Speech Recognition and Translation Using Neural Transducers—0
Show:102550
← PrevPage 77 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