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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
Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation—0
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR—0
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin—0
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition—0
Extracting Biomedical Entities from Noisy Audio Transcripts—0
Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition—0
Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models—0
Face-Dubbing++: Lip-Synchronous, Voice Preserving Translation of Videos—0
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations—0
An Investigative Study of Multi-Modal Cross-Lingual Retrieval—0
Factual Consistency Oriented Speech Recognition—0
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation—0
FairLENS: Assessing Fairness in Law Enforcement Speech Recognition—0
Fairness of Automatic Speech Recognition in Cleft Lip and Palate Speech—0
Environment-aware Reconfigurable Noise Suppression—0
Calibration of Phone Likelihoods in Automatic Speech Recognition—0
Entity resolution for noisy ASR transcripts—0
Fast and Accurate OOV Decoder on High-Level Features—0
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition—0
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter—0
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition—0
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition—0
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition—0
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation—0
Entity Linking for Spoken Language—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