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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 551–575 of 3012 papers

TitleStatusHype
WER We Stand: Benchmarking Urdu ASR Models—0
An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems—0
Augmenting Automatic Speech Recognition Models with Disfluency Detection—0
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition—0
ASR Error Correction using Large Language Models—0
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training—0
Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages—0
Exploring SSL Discrete Tokens for Multilingual ASR—0
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech—0
LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation—0
CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments—0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model—0
Linear Time Complexity Conformers with SummaryMixing for Streaming Speech RecognitionCode0
Enhancing CTC-Based Visual Speech Recognition—0
An Effective Context-Balanced Adaptation Approach for Long-Tailed Speech Recognition—0
Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking—0
Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge—0
Evaluation of real-time transcriptions using end-to-end ASR models—0
Retrieval Augmented Correction of Named Entity Speech Recognition Errors—0
An investigation of modularity for noise robustness in conformer-based ASR—0
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection—0
Quantification of stylistic differences in human- and ASR-produced transcripts of African American English—0
What is lost in Normalization? Exploring Pitfalls in Multilingual ASR Model Evaluations—0
Probing self-attention in self-supervised speech models for cross-linguistic differences—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