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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 1301–1325 of 3012 papers

TitleStatusHype
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines—0
Generative error correction for code-switching speech recognition using large language models—0
German-Arabic Speech-to-Speech Translation for Psychiatric Diagnosis—0
Gesture-Aware Zero-Shot Speech Recognition for Patients with Language Disorders—0
Entity resolution for noisy ASR transcripts—0
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation—0
Entity Linking for Spoken Language—0
Ensemble knowledge distillation of self-supervised speech models—0
Blind Signal Dereverberation for Machine Speech Recognition—0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages—0
An Investigation on Applying Acoustic Feature Conversion to ASR of Adult and Child Speech—0
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition—0
Ensemble Chinese End-to-End Spoken Language Understanding for Abnormal Event Detection from audio stream—0
Graph based manifold regularized deep neural networks for automatic speech recognition—0
Enriching ASR Lattices with POS Tags for Dependency Parsing—0
Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding—0
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation—0
AfriNames: Most ASR models "butcher" African Names—0
Enhancing Unsupervised Speech Recognition with Diffusion GANs—0
Guided contrastive self-supervised pre-training for automatic speech recognition—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments—0
Hallucination of speech recognition errors with sequence to sequence learning—0
Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Models—0
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders—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