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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 2176–2200 of 3012 papers

TitleStatusHype
The AFRL IWSLT 2020 Systems: Work-From-Home Edition—0
SimulSpeech: End-to-End Simultaneous Speech to Text Translation—0
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions—0
Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo, and Wolaytta—0
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning—0
Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations—0
Tigrinya Automatic Speech recognition with Morpheme based recognition units—0
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition—0
Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion—0
Streaming Transformer ASR with Blockwise Synchronous Inference—0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples—0
Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of Any Number of Speakers—0
End-to-End Code Switching Language Models for Automatic Speech Recognition—0
Quantization of Acoustic Model Parameters in Automatic Speech Recognition Framework—0
Towards Automated Assessment of Stuttering and Stuttering Therapy—0
Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset—0
The JHU Multi-Microphone Multi-Speaker ASR System for the CHiME-6 Challenge—0
Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification TasksCode0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors—0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation—0
Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition—0
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech—0
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR—0
Contextual RNN-T For Open Domain ASR—0
Transfer Learning for British Sign Language Modelling—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