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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 926–950 of 3012 papers

TitleStatusHype
DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction—0
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition—0
A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems—0
Contribution \`a l'\'etude de la variabilit\'e de la voix des personnes \^ag\'ees en reconnaissance automatique de la parole (Contribution to the study of elderly people's voice variability in automatic speech recognition) [in French]—0
Contrastive Semi-supervised Learning for ASR—0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model—0
Learning Video Representations using Contrastive Bidirectional Transformer—0
ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment—0
Distributed representation and estimation of WFST-based n-gram models—0
Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition—0
DNCASR: End-to-End Training for Speaker-Attributed ASR—0
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech—0
A light-weight and efficient punctuation and word casing prediction model for on-device streaming ASR—0
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding—0
Acoustic Word Disambiguation with Phonogical Features in Danish ASR—0
Continuous Speech Recognition using EEG and Video—0
Does Speech enhancement of publicly available data help build robust Speech Recognition Systems?—0
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?—0
Does Whisper understand Swiss German? An automatic, qualitative, and human evaluation—0
Continuous Pseudo-Labeling from the Start—0
Domain Adaptation of low-resource Target-Domain models using well-trained ASR Conformer Models—0
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems—0
Continuously Learning New Words in Automatic Speech Recognition—0
ATC-ANNO: Semantic Annotation for Air Traffic Control with Assistive Auto-Annotation—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