SOTAVerified

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

Showing 27762800 of 3012 papers

TitleStatusHype
Multimodal Speech Recognition for Language-Guided Embodied AgentsCode0
LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related TasksCode0
A Theory of Unsupervised Speech RecognitionCode0
Political corpus creation through automatic speech recognition on EU debatesCode0
Blank Collapse: Compressing CTC emission for the faster decodingCode0
Big model only for hard audios: Sample dependent Whisper model selection for efficient inferencesCode0
Latent Tree Language ModelCode0
Adversarial Training For Low-Resource Disfluency CorrectionCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
A Comparison of Adaptation Techniques and Recurrent Neural Network ArchitecturesCode0
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech RecognitionCode0
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTSCode0
Transformer Based Punctuation Restoration for TurkishCode0
Improving Voice Separation by Incorporating End-to-end Speech RecognitionCode0
Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search DegradationCode0
Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal ModelsCode0
A Speech Representation Anonymization Framework via Selective Noise PerturbationCode0
Multi-Speaker ASR Combining Non-Autoregressive Conformer CTC and Conditional Speaker ChainCode0
Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural NetworksCode0
Speech-Based Visual Question AnsweringCode0
Improving the Inclusivity of Dutch Speech Recognition by Fine-tuning Whisper on the JASMIN-CGN CorpusCode0
Improving RNN Transducer Modeling for End-to-End Speech RecognitionCode0
Bigger is not Always Better: The Effect of Context Size on Speech Pre-TrainingCode0
Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech RecognitionCode0
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context ModelingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1Unverified
2TM-seq2seqTest WER9.7Unverified
3CTC/attentionTest WER8.2Unverified
4LF-MMI TDNNTest WER6.7Unverified
5Whisper-LLaMATest WER6.6Unverified
6End2end ConformerTest WER3.9Unverified
7End2end ConformerTest WER3.7Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7Unverified
9CTC/AttentionTest WER1.5Unverified
10WhisperTest WER1.3Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5Unverified
2CleanMel-L-maskCER14.4Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32Unverified
2Whisper-largev3-finetunedTest WER10.82Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98Unverified