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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2051–2075 of 3012 papers

TitleStatusHype
FastEmit: Low-latency Streaming ASR with Sequence-level Emission RegularizationCode0
VenoMave: Targeted Poisoning Against Speech RecognitionCode0
Towards End-to-End Training of Automatic Speech Recognition for Nigerian PidginCode0
Cascaded Models With Cyclic Feedback For Direct Speech Translation—0
Replacing Human Audio with Synthetic Audio for On-device Unspoken Punctuation Prediction—0
Pushing the Limits of Semi-Supervised Learning for Automatic Speech RecognitionCode1
Knowledge Transfer for Efficient On-device False Trigger Mitigation—0
Ensemble Chinese End-to-End Spoken Language Understanding for Abnormal Event Detection from audio stream—0
Towards Data Distillation for End-to-end Spoken Conversational Question Answering—0
Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC—0
Non-intrusive speech intelligibility prediction using automatic speech recognition derived measures—0
Multimodal Speech Recognition with Unstructured Audio Masking—0
Lightweight End-to-End Speech Recognition from Raw Audio Data Using Sinc-Convolutions—0
Google Crowdsourced Speech Corpora and Related Open-Source Resources for Low-Resource Languages and Dialects: An OverviewCode1
Exploiting Spectral Augmentation for Code-Switched Spoken Language Identification—0
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling—0
Improving Low Resource Code-switched ASR using Augmented Code-switched TTS—0
WER we are and WER we think we are—0
Representation Learning for Sequence Data with Deep Autoencoding Predictive ComponentsCode1
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTSCode0
Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text CorpusCode0
Fine-Grained Grounding for Multimodal Speech RecognitionCode0
A Study on Lip Localization Techniques used for Lip reading from a Video—0
FluentNet: End-to-End Detection of Speech Disfluency with Deep Learning—0
End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic HandsCode0
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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