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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1976–2000 of 3012 papers

TitleStatusHype
Gaussian Kernelized Self-Attention for Long Sequence Data and Its Application to CTC-based Speech Recognition—0
Echo State Speech Recognition—0
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition—0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation—0
Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition—0
Improving speech recognition models with small samples for air traffic control systems—0
End-to-End Automatic Speech Recognition with Deep Mutual Learning—0
Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition—0
Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR—0
Content-Aware Speaker Embeddings for Speaker Diarisation—0
Hybrid phonetic-neural model for correction in speech recognition systemsCode0
Multimodal Punctuation Prediction with Contextual Dropout—0
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding—0
NUVA: A Naming Utterance Verifier for Aphasia Treatment—0
Sparsification via Compressed Sensing for Automatic Speech Recognition—0
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers—0
Effects of Layer Freezing on Transferring a Speech Recognition System to Under-resourced LanguagesCode0
A bandit approach to curriculum generation for automatic speech recognition—0
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR—0
Multi-Task Self-Supervised Pre-Training for Music Classification—0
Intermediate Loss Regularization for CTC-based Speech Recognition—0
Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with SubwordsCode0
Effects of Number of Filters of Convolutional Layers on Speech Recognition Model Accuracy—0
Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition—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