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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 1076–1100 of 3012 papers

TitleStatusHype
Automatic Speech Recognition of Low-Resource Languages Based on Chukchi—0
Scaling Up Deliberation for Multilingual ASR—0
Comparison of Soft and Hard Target RNN-T Distillation for Large-scale ASR—0
Streaming Punctuation for Long-form Dictation with Transformers—0
CTC Alignments Improve Autoregressive Translation—0
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition—0
SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-trainingCode0
Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages—0
Pronunciation Modeling of Foreign Words for Mandarin ASR by Considering the Effect of Language Transfer—0
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition—0
JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMTCode1
CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representationsCode1
Efficient acoustic feature transformation in mismatched environments using a Guided-GAN—0
Investigating the Impact of ASR Errors on Spoken Implicit Discourse Relation Recognition—0
Language-specific Effects on Automatic Speech Recognition Errors for World Englishes—0
Improving Code-switched ASR with Linguistic Information—0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling—0
Keyphrase Prediction from Video Transcripts: New Dataset and Directions—0
Zero-shot Disfluency Detection for Indian Languages—0
Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition—0
E-Branchformer: Branchformer with Enhanced merging for speech recognition—0
Blind Signal Dereverberation for Machine Speech Recognition—0
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition—0
TVLT: Textless Vision-Language TransformerCode1
An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts—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