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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1125 of 3012 papers

TitleStatusHype
Boosting the Transferability of Audio Adversarial Examples with Acoustic Representation Optimization—0
An Online Attention-based Model for Speech Recognition—0
Boosting Punctuation Restoration with Data Generation and Reinforcement Learning—0
Boosting Norwegian Automatic Speech Recognition—0
A non-expert Kaldi recipe for Vietnamese Speech Recognition System—0
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers—0
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training—0
Boosting End-to-End Multilingual Phoneme Recognition through Exploiting Universal Speech Attributes Constraints—0
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition—0
Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi—0
Error Correction in ASR using Sequence-to-Sequence Models—0
Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM—0
Advancing Arabic Speech Recognition Through Large-Scale Weakly Supervised Learning—0
A Bilingual Interactive Human Avatar Dialogue System—0
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation—0
Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person—0
Error Correction by Paying Attention to Both Acoustic and Confidence References for Automatic Speech Recognition—0
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs—0
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism—0
Error Detection in Automatic Speech Recognition—0
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept—0
ESPnet: End-to-End Speech Processing Toolkit—0
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration—0
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding—0
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks—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