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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 1801–1825 of 3012 papers

TitleStatusHype
Robustness of end-to-end Automatic Speech Recognition Models -- A Case Study using Mozilla DeepSpeech—0
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition—0
Accent Recognition with Hybrid Phonetic Features—0
Spectral modification for recognition of children’s speech undermismatched conditions—0
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
Personalized Keyphrase Detection using Speaker and Environment Information—0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition—0
Head-synchronous Decoding for Transformer-based Streaming ASR—0
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction—0
Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models—0
Quantization of Deep Neural Networks for Accurate Edge Computing—0
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from SpeechCode1
On Sampling-Based Training Criteria for Neural Language Modeling—0
Discriminative Self-training for Punctuation Prediction—0
Disfluency Detection with Unlabeled Data and Small BERT Models—0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning—0
Scene-aware Far-field Automatic Speech Recognition—0
Accented Speech Recognition: A Survey—0
Label-Synchronous Speech-to-Text Alignment for ASR Using Forward and Backward Transformers—0
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era—0
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers—0
Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition—0
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation—0
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter ItCode0
Cross-domain Speech Recognition with Unsupervised Character-level Distribution MatchingCode0
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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