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 14511475 of 3012 papers

TitleStatusHype
Improving low-resource ASR performance with untranscribed out-of-domain data0
Improving Low Resource Code-switched ASR using Augmented Code-switched TTS0
Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training0
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection0
Exploring Transfer Learning For End-to-End Spoken Language Understanding0
Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis0
A privacy-preserving method using secret key for convolutional neural network-based speech classification0
Improving Massively Multilingual ASR With Auxiliary CTC Objectives0
Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model0
Improving Membership Inference in ASR Model Auditing with Perturbed Loss Features0
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking0
Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC0
Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization0
Improving Named Entity Recognition in Spoken Dialog Systems by Context and Speech Pattern Modeling0
Improving Named Entity Transcription with Contextual LLM-based Revision0
Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation0
Improving Neural Language Models with Weight Norm Initialization and Regularization0
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network0
Improving Noise Robustness of an End-to-End Neural Model for Automatic Speech Recognition0
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction0
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition0
Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models0
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios0
A Conformer-based ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement and Speech Separation0
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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