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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 151–175 of 3012 papers

TitleStatusHype
AVATAR: Unconstrained Audiovisual Speech RecognitionCode1
LAE: Language-Aware Encoder for Monolingual and Multilingual ASRCode1
Language Models with Image Descriptors are Strong Few-Shot Video-Language LearnersCode1
Vietnamese Automatic Speech Recognition using Wav2vec 2.0Code1
Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation AssessmentCode1
Speaker Recognition in the WildCode1
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo LanguagesCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
Large-Scale Streaming End-to-End Speech Translation with Neural TransducersCode1
PriMock57: A Dataset Of Primary Care Mock ConsultationsCode1
indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languagesCode1
A Hybrid Continuity Loss to Reduce Over-Suppression for Time-domain Target Speaker ExtractionCode1
How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control CommunicationsCode1
Streaming Speaker-Attributed ASR with Token-Level Speaker EmbeddingsCode1
Integrating Lattice-Free MMI into End-to-End Speech RecognitionCode1
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERTCode1
Shifted Chunk Encoder for Transformer Based Streaming End-to-End ASRCode1
Earnings-22: A Practical Benchmark for Accents in the WildCode1
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversionCode1
Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech RecognitionCode1
Dual-Path Style Learning for End-to-End Noise-Robust Speech RecognitionCode1
Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech RecognitionCode1
Automatic Speech Recognition for Speech Assessment of Persian Preschool ChildrenCode1
Neural Predictor for Black-Box Adversarial Attacks on Speech RecognitionCode1
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question AnsweringCode1
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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