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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 2001–2025 of 3012 papers

TitleStatusHype
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration—0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages—0
Improving RNN Transducer Based ASR with Auxiliary TasksCode1
Alignment Restricted Streaming Recurrent Neural Network Transducer—0
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework—0
Incremental Machine Speech Chain Towards Enabling Listening while Speaking in Real-time—0
Data Augmentation for End-to-end Code-switching Speech Recognition—0
Sequence-to-Sequence Learning via Attention Transfer for Incremental Speech Recognition—0
Improving RNN transducer with normalized jointer network—0
Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition—0
Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition—0
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis—0
Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features—0
Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASRCode1
Warped Language Models for Noise Robust Language Understanding—0
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation—0
Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech TranslationCode1
DNN-Based Semantic Model for Rescoring N-best Speech Recognition List—0
ELITR: European Live Translator—0
Punctuation Restoration using Transformer Models for High-and Low-Resource LanguagesCode1
Impact of ASR on Alzheimer’s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others—0
Improving End-to-End Bangla Speech Recognition with Semi-supervised Training—0
Direct Segmentation Models for Streaming Speech TranslationCode0
Effectively pretraining a speech translation decoder with Machine Translation data—0
Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced LanguagesCode0
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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