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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 1901–1925 of 3012 papers

TitleStatusHype
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition—0
Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks—0
Thoughts on the potential to compensate a hearing loss in noise—0
Evolutionary optimization of contexts for phonetic correction in speech recognition systems—0
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial DomainCode0
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model—0
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition—0
Gaussian Kernelized Self-Attention for Long Sequence Data and Its Application to CTC-based Speech Recognition—0
Echo State Speech Recognition—0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition—0
End-to-End Automatic Speech Recognition with Deep Mutual Learning—0
Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation—0
Improving speech recognition models with small samples for air traffic control systems—0
Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition—0
Multimodal Punctuation Prediction with Contextual Dropout—0
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding—0
Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR—0
Hybrid phonetic-neural model for correction in speech recognition systemsCode0
End-to-end Audio-visual Speech Recognition with ConformersCode1
Transformer Language Models with LSTM-based Cross-utterance Information RepresentationCode1
Content-Aware Speaker Embeddings for Speaker Diarisation—0
An Investigation of End-to-End Models for Robust Speech RecognitionCode1
Dompteur: Taming Audio Adversarial ExamplesCode1
NUVA: A Naming Utterance Verifier for Aphasia Treatment—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