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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 1951–2000 of 3012 papers

TitleStatusHype
An Approach to Improve Robustness of NLP Systems against ASR Errors—0
Voice Privacy with Smart Digital Assistants in Educational Settings—0
Hallucination of speech recognition errors with sequence to sequence learning—0
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition SystemsCode0
Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents—0
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation—0
EdgeCRNN: an edgecomputing oriented model of acoustic feature enhancement for keyword spotting—0
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training—0
Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition—0
Learning Word-Level Confidence For Subword End-to-End ASR—0
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning—0
Contrastive Semi-supervised Learning for ASR—0
An Ultra-low Power RNN Classifier for Always-On Voice Wake-Up Detection Robust to Real-World Scenarios—0
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration—0
Incorporating VAD into ASR System by Multi-task Learning—0
Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition—0
Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition—0
Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event LocalizationCode0
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition—0
Meta-Learning for improving rare word recognition in end-to-end ASR—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
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