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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 1251–1275 of 3012 papers

TitleStatusHype
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition—0
Fine-Tuning Automatic Speech Recognition for People with Parkinson's: An Effective Strategy for Enhancing Speech Technology Accessibility—0
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin—0
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter—0
Fast and Robust Unsupervised Contextual Biasing for Speech Recognition—0
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition—0
Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts—0
Fast and Accurate OOV Decoder on High-Level Features—0
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging—0
Flexible Multichannel Speech Enhancement for Noise-Robust Frontend—0
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios—0
FluentNet: End-to-End Detection of Speech Disfluency with Deep Learning—0
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models—0
Foreign English Accent Adjustment by Learning Phonetic Patterns—0
Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans—0
Free English and Czech telephone speech corpus shared under the CC-BY-SA 3.0 license—0
Calibration of Phone Likelihoods in Automatic Speech Recognition—0
Frequency Domain Multi-channel Acoustic Modeling for Distant Speech Recognition—0
From Audio to Semantics: Approaches to end-to-end spoken language understanding—0
From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition—0
From Human Language Technology to Human Language Science—0
From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition—0
From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy—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