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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 2551–2600 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
Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation—0
Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization—0
Cycle-consistency training for end-to-end speech recognition—0
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation—0
Improving the Robustness of Speech Translation—0
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model—0
Introspection for convolutional automatic speech recognition—0
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition—0
Tropical Modeling of Weighted Transducer Algorithms on Graphs—0
End-to-End Feedback Loss in Speech Chain Framework via Straight-Through Estimator—0
Towards End-to-End Code-Switching Speech Recognition—0
Bi-Directional Lattice Recurrent Neural Networks for Confidence EstimationCode0
Towards End-to-end Automatic Code-Switching Speech Recognition—0
Contextual Speech Recognition with Difficult Negative Training Examples—0
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition—0
Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative TrainingCode0
Speaker Selective Beamformer with Keyword Mask Estimation—0
The MeMAD Submission to the IWSLT 2018 Speech Translation Task—0
A Deep Generative Acoustic Model for Compositional Automatic Speech Recognition—0
Semi-supervised acoustic model training for speech with code-switching—0
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition—0
Cycle-Consistent GAN Front-End to Improve ASR Robustness to Perturbed Speech—0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features—0
ROBUST SPEECH COMMAND RECOGNITION USING LABEL-DRIVEN TIME-FREQUENCY MASKING—0
How transferable are features in convolutional neural network acoustic models across languages?—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