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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 1201–1225 of 3012 papers

TitleStatusHype
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter—0
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech—0
Cascaded Models With Cyclic Feedback For Direct Speech Translation—0
Cascaded encoders for unifying streaming and non-streaming ASR—0
A review of on-device fully neural end-to-end automatic speech recognition algorithms—0
Adversarial Training for Multilingual Acoustic Modeling—0
Accented Speech Recognition: A Survey—0
Cascaded Cross-Modal Transformer for Request and Complaint Detection—0
Are Transformers in Pre-trained LM A Good ASR Encoder? An Empirical Study—0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition—0
CarneliNet: Neural Mixture Model for Automatic Speech Recognition—0
Adversarial synthesis based data-augmentation for code-switched spoken language identification—0
Capturing Multi-Resolution Context by Dilated Self-Attention—0
Capitalization and Punctuation Restoration: a Survey—0
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers—0
Can Whisper perform speech-based in-context learning?—0
Are disentangled representations all you need to build speaker anonymization systems?—0
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion—0
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition—0
A Recorded Debating Dataset—0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling—0
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition—0
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?—0
Adversarial Speaker Adaptation—0
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition—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