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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 2876–2900 of 3012 papers

TitleStatusHype
Bi-directional Context-Enhanced Speech Large Language Models for Multilingual Conversational ASR—0
Bidirectional Representations for Low Resource Spoken Language Understanding—0
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition—0
Bilingual End-to-End ASR with Byte-Level Subwords—0
Bilingual Streaming ASR with Grapheme units and Auxiliary Monolingual Loss—0
Binarized LSTM Language Model—0
Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking—0
Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024—0
Blending LSTMs into CNNs—0
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation—0
Blind Signal Dereverberation for Machine Speech Recognition—0
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation—0
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition—0
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism—0
Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM—0
Boosting End-to-End Multilingual Phoneme Recognition through Exploiting Universal Speech Attributes Constraints—0
Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training—0
Boosting Norwegian Automatic Speech Recognition—0
Boosting Punctuation Restoration with Data Generation and Reinforcement Learning—0
Boosting the Transferability of Audio Adversarial Examples with Acoustic Representation Optimization—0
Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio—0
Brain Signals to Rescue Aphasia, Apraxia and Dysarthria Speech Recognition—0
Breaking the Data Barrier: Towards Robust Speech Translation via Adversarial Stability Training—0
Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages—0
Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts—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