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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
Multiple topic identification in human/human conversations—0
The Recognition Of Persian Phonemes Using PPNet—0
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs—0
End-to-end contextual speech recognition using class language models and a token passing decoder—0
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices—0
Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR—0
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition—0
Speech recognition with quaternion neural networks—0
WEST: Word Encoded Sequence Transducers—0
Investigating the Effects of Word Substitution Errors on Sentence EmbeddingsCode0
Exploring RNN-Transducer for Chinese Speech Recognition—0
Corpus Phonetics Tutorial—0
An Online Attention-based Model for Speech Recognition—0
Multi-encoder multi-resolution framework for end-to-end speech recognition—0
Stream attention-based multi-array end-to-end speech recognition—0
Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling—0
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition—0
Multimodal Grounding for Sequence-to-Sequence Speech RecognitionCode0
Confusion2Vec: Towards Enriching Vector Space Word Representations with Representational Ambiguities—0
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments—0
Analysis of Multilingual Sequence-to-Sequence speech recognition systems—0
Discriminative training of RNNLMs with the average word error criterion—0
Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech RecognitionCode0
When CTC Training Meets Acoustic Landmarks—0
End-to-End Monaural Multi-speaker ASR System without Pretraining—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