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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 1676–1700 of 3012 papers

TitleStatusHype
On Prosody Modeling for ASR+TTS based Voice Conversion—0
On Sampling-Based Training Criteria for Neural Language Modeling—0
On Spoken Language Understanding Systems for Low Resourced Languages—0
On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models—0
On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition—0
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech—0
On the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architectures—0
On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition—0
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era—0
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition—0
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition—0
On the N-gram Approximation of Pre-trained Language Models—0
On the Relevance of Auditory-Based Gabor Features for Deep Learning in Automatic Speech Recognition—0
On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition—0
On the Transferability of Whisper-based Representations for "In-the-Wild" Cross-Task Downstream Speech Applications—0
On the Usefulness of Self-Attention for Automatic Speech Recognition with Transformers—0
On the verbalization patterns of part-whole relations in isiZulu—0
ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks—0
ON-TRAC’ systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks—0
On using 2D sequence-to-sequence models for speech recognition—0
On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches—0
OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking—0
Open ASR for Icelandic: Resources and a Baseline System—0
Open Challenge for Correcting Errors of Speech Recognition Systems—0
Open Implementation and Study of BEST-RQ for Speech Processing—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