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 13011325 of 3012 papers

TitleStatusHype
Improved far-field speech recognition using Joint Variational Autoencoder0
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment0
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation0
An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition0
Disappeared Command: Spoofing Attack On Automatic Speech Recognition Systems with Sound Masking0
Automated speech tools for helping communities process restricted-access corpora for language revival efforts0
Lombard Effect for Bilingual Speakers in Cantonese and English: importance of spectro-temporal features0
HuBERT-EE: Early Exiting HuBERT for Efficient Speech RecognitionCode0
A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes0
Study of Indian English Pronunciation Variabilities relative to Received Pronunciation0
Self-critical Sequence Training for Automatic Speech Recognition0
ASR in German: A Detailed Error Analysis0
Large-Scale Streaming End-to-End Speech Translation with Neural TransducersCode1
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data0
Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition0
Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility PredictionCode0
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser0
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model0
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition0
Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation0
Unsupervised Data Selection via Discrete Speech Representation for ASR0
Audio-visual multi-channel speech separation, dereverberation and recognition0
Towards End-to-end Unsupervised Speech Recognition0
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation0
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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