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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

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Showing 1551–1575 of 3012 papers

TitleStatusHype
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition—0
Asynchronous Decentralized Distributed Training of Acoustic Models—0
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement—0
An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR—0
Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition—0
AequeVox: Automated Fairness Testing of Speech Recognition SystemsCode0
Efficient Sequence Training of Attention Models using Approximative Recombination—0
ViraPart: A Text Refinement Framework for Automatic Speech Recognition and Natural Language Processing Tasks in Persian—0
Automatic Learning of Subword Dependent Model Scales—0
Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages—0
A Unified Speaker Adaptation Approach for ASRCode0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes—0
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet—0
CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian PortugueseCode1
Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts—0
Sub-word Level Lip Reading With Visual Attention—0
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language ProcessingCode1
Continual learning using lattice-free MMI for speech recognition—0
All-neural beamformer for continuous speech separation—0
Prompt-tuning in ASR systems for efficient domain-adaptation—0
Improving Character Error Rate Is Not Equal to Having Clean Speech: Speech Enhancement for ASR Systems with Black-box Acoustic Models—0
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control CommunicationsCode1
Word Order Does Not Matter For Speech Recognition—0
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition—0
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All datasetsLRS2RealMANSagaleeHUI speech corpusLRS3-TEDM-AILabs speech datasetThe Spoken Wikipedia CorporaVoxforge GermanVoxPopuli

Benchmark Results

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