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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 2976–3000 of 3012 papers

TitleStatusHype
Code Switched and Code Mixed Speech Recognition for Indic languages—0
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences—0
Code-Switching Detection Using ASR-Generated Language Posteriors—0
Codeswitching Detection via Lexical Features in Conditional Random Fields—0
Code-Switching Detection with Data-Augmented Acoustic and Language Models—0
Code-Switching Text Generation and Injection in Mandarin-English ASR—0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants—0
Combining Human Inputters and Language Services to provide Multi-language support system for International Symposiums—0
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition—0
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
Comparative Analysis of the wav2vec 2.0 Feature Extractor—0
Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task—0
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model—0
Comparing Discrete and Continuous Space LLMs for Speech Recognition—0
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures—0
Comparing Two Basic Methods for Discriminating Between Similar Languages and Varieties—0
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary—0
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR—0
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch—0
Comparison of Soft and Hard Target RNN-T Distillation for Large-scale ASR—0
Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation—0
Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding—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