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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 1351–1375 of 3012 papers

TitleStatusHype
Meta Auxiliary Learning for Low-resource Spoken Language Understanding—0
Improving the Training Recipe for a Robust Conformer-based Hybrid Model—0
Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi—0
On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Mode—0
Confidence Score Based Conformer Speaker Adaptation for Speech Recognition—0
Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems—0
A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Data—0
Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training—0
The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition—0
Boosting Cross-Domain Speech Recognition with Self-SupervisionCode0
Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warping—0
Decoupled Federated Learning for ASR with Non-IID Data—0
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR—0
A CTC Triggered Siamese Network with Spatial-Temporal Dropout for Speech Recognition—0
Transformer-based Automatic Speech Recognition of Formal and Colloquial Czech in MALACH Project—0
Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech—0
Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition—0
Residual Language Model for End-to-end Speech Recognition—0
The ZevoMOS entry to VoiceMOS Challenge 2022—0
Toward Zero Oracle Word Error Rate on the Switchboard Benchmark—0
Investigation of Ensemble features of Self-Supervised Pretrained Models for Automatic Speech Recognition—0
Context-based out-of-vocabulary word recovery for ASR systems in Indian languages—0
Face-Dubbing++: Lip-Synchronous, Voice Preserving Translation of Videos—0
Joint Encoder-Decoder Self-Supervised Pre-training for ASR—0
LegoNN: Building Modular Encoder-Decoder Models—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