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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 2926–2950 of 3012 papers

TitleStatusHype
Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis—0
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline—0
BUT Opensat 2019 Speech Recognition System—0
BUT System for the MLC-SLM Challenge—0
Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts—0
Byte Pair Encoding Is All You Need For Automatic Bengali Speech Recognition—0
CAFE A Novel Code switching Dataset for Algerian Dialect French and English—0
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection—0
Calibration of Phone Likelihoods in Automatic Speech Recognition—0
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition—0
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin—0
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?—0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling—0
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition—0
Can Whisper perform speech-based in-context learning?—0
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers—0
Capitalization and Punctuation Restoration: a Survey—0
Capturing Multi-Resolution Context by Dilated Self-Attention—0
CarneliNet: Neural Mixture Model for Automatic Speech Recognition—0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition—0
Cascaded Cross-Modal Transformer for Request and Complaint Detection—0
Cascaded encoders for unifying streaming and non-streaming ASR—0
Cascaded Models With Cyclic Feedback For Direct Speech Translation—0
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter—0
CASSANDRA: A multipurpose configurable voice-enabled human-computer-interface—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