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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 901–925 of 3012 papers

TitleStatusHype
Differentially Private Speaker Anonymization—0
Digits micro-model for accurate and secure transactions—0
Dilated U-net based approach for multichannel speech enhancement from First-Order Ambisonics recordings—0
Direct Acoustics-to-Word Models for English Conversational Speech Recognition—0
Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech—0
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization—0
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers—0
Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments—0
Direct Speech to Speech Translation: A Review—0
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis—0
Disappeared Command: Spoofing Attack On Automatic Speech Recognition Systems with Sound Masking—0
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset—0
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool—0
Discovering Canonical Indian English Accents: A Crowdsourcing-based Approach—0
Can Whisper perform speech-based in-context learning?—0
DiscreTalk: Text-to-Speech as a Machine Translation Problem—0
Automatic Speech Recognition for Biomedical Data in Bengali Language—0
Are disentangled representations all you need to build speaker anonymization systems?—0
Discriminative Self-training for Punctuation Prediction—0
Discriminative Speech Recognition Rescoring with Pre-trained Language Models—0
Discriminative training of RNNLMs with the average word error criterion—0
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation—0
Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion—0
Disentangling Prosody Representations with Unsupervised Speech Reconstruction—0
Does Whisper understand Swiss German? An automatic, qualitative, and human evaluation—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