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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 2376–2400 of 3012 papers

TitleStatusHype
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model—0
Analyzing ASR pretraining for low-resource speech-to-text translation—0
RNN based Incremental Online Spoken Language Understanding—0
G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR—0
Robust Neural Machine Translation for Clean and Noisy Speech Transcripts—0
Word-level Embeddings for Cross-Task Transfer Learning in Speech ProcessingCode0
AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks—0
Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models—0
Multi-Talker MVDR Beamforming Based on Extended Complex Gaussian Mixture Model—0
Transformer ASR with Contextual Block Processing—0
Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition—0
Analyzing Large Receptive Field Convolutional Networks for Distant Speech Recognition—0
VAIS ASR: Building a conversational speech recognition system using language model combination—0
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems—0
Query-by-example on-device keyword spotting—0
One-To-Many Multilingual End-to-end Speech Translation—0
A Case Study on Combining ASR and Visual Features for Generating Instructional Video Captions—0
Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning—0
Modeling Confidence in Sequence-to-Sequence Models—0
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System—0
From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition—0
使用生成對抗網路於強健式自動語音辨識的應用(Exploiting Generative Adversarial Network for Robustness Automatic Speech Recognition)—0
Multilingual End-to-End Speech Translation—0
End-to-End Code-Switching ASR for Low-Resourced Language Pairs—0
Improving RNN Transducer Modeling for End-to-End Speech RecognitionCode0
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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