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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 2551–2575 of 3012 papers

TitleStatusHype
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers—0
Advancing Arabic Speech Recognition Through Large-Scale Weakly Supervised Learning—0
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers—0
Advancing Hearing Assessment: An ASR-Based Frequency-Specific Speech Test for Diagnosing Presbycusis—0
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy—0
Advancing Multi-talker ASR Performance with Large Language Models—0
Advancing Speech Recognition With No Speech Or With Noisy Speech—0
Adversarial Attacks and Defenses for Speech Recognition Systems—0
Adversarial Attacks on ASR Systems: An Overview—0
Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization—0
Adversarial Joint Training with Self-Attention Mechanism for Robust End-to-End Speech Recognition—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
Adversarial Speaker Adaptation—0
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion—0
Adversarial synthesis based data-augmentation for code-switched spoken language identification—0
Adversarial Training for Multilingual Acoustic Modeling—0
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model—0
Advocating Character Error Rate for Multilingual ASR Evaluation—0
Affect Recognition in Conversations Using Large Language Models—0
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding—0
AfriNames: Most ASR models "butcher" African Names—0
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR—0
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond—0
AGADIR: Towards Array-Geometry Agnostic Directional Speech Recognition—0
A GEN AI Framework for Medical Note Generation—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