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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 651–675 of 3012 papers

TitleStatusHype
Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model—0
Automatic Speech Recognition for Irish: the ABAIR-ÉIST System—0
Automatic Speech Recognition for Humanitarian Applications in Somali—0
Automatic Speech Recognition for Hindi—0
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition—0
Automatic Speech Recognition for Biomedical Data in Bengali Language—0
Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions—0
Automatic Speech Recognition Errors as a Predictor of L2 Listening Difficulties—0
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset—0
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings—0
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings—0
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis—0
Automatic Speech Recognition (ASR) for the Diagnosis of pronunciation of Speech Sound Disorders in Korean children—0
Automatic Speech Recognition: A Shifted Role in Early Speech Intervention?—0
Analyzing Large Receptive Field Convolutional Networks for Distant Speech Recognition—0
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages—0
Analyzing ASR pretraining for low-resource speech-to-text translation—0
Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges—0
Analysis of Self-Attention Head Diversity for Conformer-based Automatic Speech Recognition—0
Adapting Multi-Lingual ASR Models for Handling Multiple Talkers—0
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks—0
使用字典學習法於強健性語音辨識 (The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]—0
Transformer-based Cascaded Multimodal Speech Translation—0
Automatic recognition of suprasegmentals in speech—0
Automatic recognition of element classes and boundaries in the birdsong with variable sequences—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