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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 651–675 of 3012 papers

TitleStatusHype
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers—0
Construction of a Large-scale Japanese ASR Corpus on TV Recordings—0
Challenges in Speech Recognition and Translation of High-Value Low-Density Polysynthetic Languages—0
Clinical Dialogue Transcription Error Correction using Seq2Seq Models—0
Challenges and Opportunities of Speech Recognition for Bengali Language—0
Challenges and Opportunities in Multi-device Speech Processing—0
Chain-of-Thought Prompting for Speech Translation—0
ArzEn: A Speech Corpus for Code-switched Egyptian Arabic-English—0
Advocating Character Error Rate for Multilingual ASR Evaluation—0
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments—0
Coarse-To-Fine And Cross-Lingual ASR Transfer—0
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations—0
Chain of Correction for Full-text Speech Recognition with Large Language Models—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
ASR-based CALL systems and learner speech data: new resources and opportunities for research and development in second language learning—0
CEASR: A Corpus for Evaluating Automatic Speech Recognition—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
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting—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