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 726750 of 3012 papers

TitleStatusHype
A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC VideosCode0
Model Adaptation for ASR in low-resource Indian Languages0
Representation Learning With Hidden Unit Clustering For Low Resource Speech Applications0
Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech Recognition0
Exploring the Integration of Large Language Models into Automatic Speech Recognition Systems: An Empirical Study0
Speech Diarization and ASR with GMM0
Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments0
Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture0
Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos0
Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework0
Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Data0
Boosting Norwegian Automatic Speech Recognition0
Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages0
Don't Stop Self-Supervision: Accent Adaptation of Speech Representations via Residual Adapters0
Accelerating Transducers through Adjacent Token Merging0
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning0
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios0
NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive LearningCode1
Exploring the Role of Audio in Video Captioning0
Federated Self-Learning with Weak Supervision for Speech Recognition0
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-SupervisionCode1
Learning When to Trust Which Teacher for Weakly Supervised ASR0
Mixture Encoder for Joint Speech Separation and Recognition0
Rehearsal-Free Online Continual Learning for Automatic Speech RecognitionCode0
MobileASR: A resource-aware on-device learning framework for user voice personalization applications on mobile phones0
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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