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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 1801–1850 of 3012 papers

TitleStatusHype
SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions—0
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning—0
Pushing the Limits of Non-Autoregressive Speech Recognition—0
Pynini: A Python library for weighted finite-state grammar compilation—0
QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus—0
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus—0
Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations—0
Quality Estimation for Automatic Speech Recognition—0
Quantification of stylistic differences in human- and ASR-produced transcripts of African American English—0
Quantization of Acoustic Model Parameters in Automatic Speech Recognition Framework—0
Quantization of Deep Neural Networks for Accurate Edge Computing—0
Quaternion Neural Networks for Multi-channel Distant Speech Recognition—0
Query-by-example on-device keyword spotting—0
Quran Recitation Recognition using End-to-End Deep Learning—0
RACAI Entry for the IWSLT 2016 Shared Task—0
Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals—0
Reading Miscue Detection in Primary School through Automatic Speech Recognition—0
Real-Time Keyword Extraction from Conversations—0
Real-Time Neural Voice Camouflage—0
Real to H-space Encoder for Speech Recognition—0
Reassessing Noise Augmentation Methods in the Context of Adversarial Speech—0
Recent Advances in End-to-End Automatic Speech Recognition—0
Recent Progress in the CUHK Dysarthric Speech Recognition System—0
Recognition of Isolated Words using Zernike and MFCC features for Audio Visual Speech Recognition—0
Recognize Foreign Low-Frequency Words with Similar Pairs—0
Recognizing long-form speech using streaming end-to-end models—0
Recognizing Multi-talker Speech with Permutation Invariant Training—0
Reconnaissance automatique de la parole : g\'en\'eration des prononciations non natives pour l'enrichissement du lexique (In this study we propose a method for lexicon adaptation in order to improve the automatic speech recognition (ASR) of non-native speakers)—0
Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcripts—0
Recurrent Deep Stacking Networks for Speech Recognition—0
Recurrent Neural Network Training with Dark Knowledge Transfer—0
RED-ACE: Robust Error Detection for ASR using Confidence Embeddings—0
Reducing Exposure Bias in Training Recurrent Neural Network Transducers—0
Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation—0
Reducing language context confusion for end-to-end code-switching automatic speech recognition—0
Reducing Spelling Inconsistencies in Code-Switching ASR using Contextualized CTC Loss—0
Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance—0
Refining Automatic Speech Recognition System for older adults—0
Regularizing Learnable Feature Extraction for Automatic Speech Recognition—0
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition—0
Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions—0
Remember the context! ASR slot error correction through memorization—0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients—0
Replacing Human Audio with Synthetic Audio for On-device Unspoken Punctuation Prediction—0
Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech Recognition—0
Representation Learning With Hidden Unit Clustering For Low Resource Speech Applications—0
RescoreBERT: Discriminative Speech Recognition Rescoring with BERT—0
Research Challenges in Building a Voice-based Artificial Personal Shopper - Position Paper—0
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech—0
Residual Convolutional CTC Networks for Automatic 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