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 501525 of 3012 papers

TitleStatusHype
Discrete Cross-Modal Alignment Enables Zero-Shot Speech TranslationCode0
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTSCode0
DoCIA: An Online Document-Level Context Incorporation Agent for Speech TranslationCode0
Direct Segmentation Models for Streaming Speech TranslationCode0
Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition SystemsCode0
Towards End-to-End Speech Recognition with Deep Convolutional Neural NetworksCode0
BERT Attends the Conversation: Improving Low-Resource Conversational ASRCode0
A Speech Representation Anonymization Framework via Selective Noise PerturbationCode0
Did you hear that? Adversarial Examples Against Automatic Speech RecognitionCode0
DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European LanguagesCode0
Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal ModelsCode0
Detecting Adversarial Examples for Speech Recognition via Uncertainty QuantificationCode0
DiaCorrect: End-to-end error correction for speaker diarizationCode0
Discovering Phonetic Inventories with Crosslingual Automatic Speech RecognitionCode0
Data augmentation using prosody and false starts to recognize non-native children's speechCode0
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial DomainCode0
Deep Learning for Audio Signal ProcessingCode0
A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational AICode0
Cross-domain Speech Recognition with Unsupervised Character-level Distribution MatchingCode0
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LMCode0
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language UnderstandingCode0
Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with SubwordsCode0
A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech RecognitionCode0
Continual Learning for Monolingual End-to-End Automatic Speech RecognitionCode0
Coupled Training of Sequence-to-Sequence Models for Accented Speech RecognitionCode0
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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