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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 1826–1850 of 3012 papers

TitleStatusHype
User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis—0
Using Ambiguity Detection to Streamline Linguistic Annotation—0
Using Automatic Speech Recognition in Spoken Corpus Curation—0
Using English Acoustic Models for Hindi Automatic Speech Recognition—0
Using heterogeneity in semi-supervised transcription hypotheses to improve code-switched speech recognition—0
Using Kaldi for Automatic Speech Recognition of Conversational Austrian German—0
Using Large Language Model for End-to-End Chinese ASR and NER—0
Using multiple ASR hypotheses to boost i18n NLU performance—0
Using multi-task learning to improve the performance of acoustic-to-word and conventional hybrid models—0
Using Related Languages to Enhance Statistical Language Models—0
Using Spoken Word Posterior Features in Neural Machine Translation—0
Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systems—0
Using Text Injection to Improve Recognition of Personal Identifiers in Speech—0
Using the TED Talks to Evaluate Spoken Post-editing of Machine Translation—0
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models—0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders—0
Utterance-level neural confidence measure for end-to-end children speech recognition—0
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones—0
V2S attack: building DNN-based voice conversion from automatic speaker verification—0
VAD-free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording—0
VADOI:Voice-Activity-Detection Overlapping Inference For End-to-end Long-form Speech Recognition—0
VAIS ASR: Building a conversational speech recognition system using language model combination—0
VAKTA-SETU: A Speech-to-Speech Machine Translation Service in Select Indic Languages—0
ValSub: Subsampling Validation Data to Mitigate Forgetting during ASR Personalization—0
VarArray Meets t-SOT: Advancing the State of the Art of Streaming Distant Conversational 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