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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 601–625 of 3012 papers

TitleStatusHype
An Effective Context-Balanced Adaptation Approach for Long-Tailed Speech Recognition—0
AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning—0
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading—0
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution—0
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition—0
Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition—0
Automating speech reception threshold measurements using automatic speech recognition—0
An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering—0
Automatic Viseme Vocabulary Construction to Enhance Continuous Lip-reading—0
Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language—0
An Attentional Model for Speech Translation Without Transcription—0
Adaptive Multi-Corpora Language Model Training for Speech Recognition—0
Automatic Syllabification for Manipuri language—0
Automatic Spoken Language Identification using a Time-Delay Neural Network—0
Anatomy of Industrial Scale Multilingual ASR—0
Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies—0
Automatic Speech Recognition with BERT and CTC Transformers: A Review—0
An ASR-free Fluency Scoring Approach with Self-Supervised Learning—0
Adaptive Frequency Cepstral Coefficients for Word Mispronunciation Detection—0
A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings—0
A bandit approach to curriculum generation for automatic speech recognition—0
Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation—0
Automatic Speech Recognition Using Template Model for Man-Machine Interface—0
類神經網路訓練結合環境群集及專家混合系統於強健性語音辨識(Automatic Speech Recognition using Neural Network based Acoustic Model with the Environment Clustering and Mixture of Experts Algorithms) [In Chinese]—0
An ASR-Based Tutor for Learning to Read: How to Optimize Feedback to First Graders—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