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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 2226–2250 of 3012 papers

TitleStatusHype
Parallel Corpus for Japanese Spoken-to-Written Style Conversion—0
Evaluation of Off-the-shelf Speech Recognizers Across Diverse Dialogue Domains—0
Using Automatic Speech Recognition in Spoken Corpus Curation—0
Corpus Generation for Voice Command in Smart Home and the Effect of Speech Synthesis on End-to-End SLU—0
On Construction of the ASR-oriented Indian English Pronunciation Dictionary—0
Analysis of GlobalPhone and Ethiopian Languages Speech Corpora for Multilingual ASR—0
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo and Wolaytta—0
Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language—0
A CLARIN Transcription Portal for Interview Data—0
Where are we in Named Entity Recognition from Speech?—0
Malayalam Speech Corpus: Design and Development for Dravidian Language—0
Towards Building an Automatic Transcription System for Language Documentation: Experiences from Muyu—0
ArzEn: A Speech Corpus for Code-switched Egyptian Arabic-English—0
Samr\'omur: Crowd-sourcing Data Collection for Icelandic Speech Recognition—0
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information—0
LinTO Platform: A Smart Open Voice Assistant for Business Environments—0
Fully Convolutional ASR for Less-Resourced Endangered Languages—0
Crossing the SSH Bridge with Interview Data—0
Corpora for Cross-Language Information Retrieval in Six Less-Resourced Languages—0
An Investigative Study of Multi-Modal Cross-Lingual Retrieval—0
Automatically Assess Children's Reading Skills—0
Challenges of Applying Automatic Speech Recognition for Transcribing EU Parliament Committee Meetings: A Pilot Study—0
Phonemic Transcription of Low-Resource Languages: To What Extent can Preprocessing be Automated?—0
Open-Source High Quality Speech Datasets for Basque, Catalan and Galician—0
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech—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