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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 2751–2775 of 3012 papers

TitleStatusHype
Multilingual Speech Recognition With A Single End-To-End Model—0
Dual Language Models for Code Switched Speech Recognition—0
Unsupervised Method for Improving Arabic Speech Recognition Systems—0
A Parallel Recurrent Neural Network for Language Modeling with POS Tags—0
Joint Learning of Dialog Act Segmentation and Recognition in Spoken Dialog Using Neural Networks—0
Improving Black-box Speech Recognition using Semantic Parsing—0
Sequence-to-Sequence ASR Optimization via Reinforcement Learning—0
A Study of All-Convolutional Encoders for Connectionist Temporal Classification—0
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition—0
Syntactic and Semantic Features For Code-Switching Factored Language Models—0
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts—0
Attention-based Wav2Text with Feature Transfer Learning—0
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential DataCode0
WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition—0
Language Modeling with Highway LSTM—0
A Recorded Debating Dataset—0
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition SystemsCode0
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks—0
Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the ArtCode0
Amharic-English Speech Translation in Tourism Domain—0
Word Transduction for Addressing the OOV Problem in Machine Translation for Similar Resource-Scarce Languages—0
Improving Machine Translation Quality Estimation with Neural Network Features—0
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017—0
A Text Normalisation System for Non-Standard English Words—0
Enriching ASR Lattices with POS Tags for Dependency Parsing—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