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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 1501–1525 of 3012 papers

TitleStatusHype
Meta Auxiliary Learning for Low-resource Spoken Language Understanding—0
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR—0
Meta Learning for End-to-End Low-Resource Speech Recognition—0
Meta-Learning for improving rare word recognition in end-to-end ASR—0
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction—0
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation—0
Minimally Supervised Written-to-Spoken Text Normalization—0
Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition—0
Mitigating Noisy Inputs for Question Answering—0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems—0
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition—0
Mixture Encoder for Joint Speech Separation and Recognition—0
Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription—0
Mixture of LoRA Experts for Low-Resourced Multi-Accent Automatic Speech Recognition—0
Mixtures of Deep Neural Experts for Automated Speech Scoring—0
MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition—0
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding—0
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition—0
MLP-based architecture with variable length input for automatic speech recognition—0
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets—0
MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition—0
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition—0
MobileASR: A resource-aware on-device learning framework for user voice personalization applications on mobile phones—0
MOCCA: Measure of Confidence for Corpus Analysis - Automatic Reliability Check of Transcript and Automatic Segmentation—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