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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 451–500 of 3012 papers

TitleStatusHype
Automatic Speech Recognition: A Shifted Role in Early Speech Intervention?—0
Automatic Speech Recognition (ASR) for the Diagnosis of pronunciation of Speech Sound Disorders in Korean children—0
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation—0
Automatic Speech Recognition Biases in Newcastle English: an Error Analysis—0
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset—0
Automatic Speech Recognition Errors as a Predictor of L2 Listening Difficulties—0
Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions—0
Automatic Speech Recognition for Biomedical Data in Bengali Language—0
Automatic Speech Recognition for Hindi—0
Automatic Speech Recognition for Humanitarian Applications in Somali—0
Automatic Speech Recognition for Irish: the ABAIR-ÉIST System—0
Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model—0
Automatic Speech Recognition for Non-Native English: Accuracy and Disfluency Handling—0
Attentive listening system with backchanneling, response generation and flexible turn-taking—0
Automatic Speech Recognition for the Ika Language—0
Activity focused Speech Recognition of Preschool Children in Early Childhood Classrooms—0
Automatic Speech Recognition for Uyghur through Multilingual Acoustic Modeling—0
Automatic Speech Recognition in German: A Detailed Error Analysis—0
Attentive Adversarial Learning for Domain-Invariant Training—0
Automatic speech recognition in the diagnosis of primary progressive aphasia—0
Automatic Speech Recognition of African American English: Lexical and Contextual Effects—0
Automatic Speech Recognition of Low-Resource Languages Based on Chukchi—0
Automatic Speech Recognition on a Firefighter TETRA Broadcast Channel—0
Automatic Speech Recognition System-Independent Word Error Rate Estimation—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Automatic Speech Recognition And Limited Vocabulary: A Survey—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
Automatic Speech Recognition Using Template Model for Man-Machine Interface—0
Automatic Speech Recognition with BERT and CTC Transformers: A Review—0
Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies—0
Automatic Spoken Language Identification using a Time-Delay Neural Network—0
Automatic Syllabification for Manipuri language—0
Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language—0
Automatic Viseme Vocabulary Construction to Enhance Continuous Lip-reading—0
Automating speech reception threshold measurements using automatic speech recognition—0
Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition—0
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading—0
Attention Enhanced Citrinet for Speech Recognition—0
AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning—0
Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework—0
AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition—0
A Vietnamese Dialog Act Corpus Based on ISO 24617-2 standard—0
A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers—0
A Closer Look at Audio-Visual Multi-Person Speech Recognition and Active Speaker Selection—0
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR—0
Attention-based Wav2Text with Feature Transfer Learning—0
Attention based on-device streaming speech recognition with large speech corpus—0
Back-Translation-Style Data Augmentation for End-to-End ASR—0
BanglaNum -- A Public Dataset for Bengali Digit Recognition from Speech—0
Alignment Restricted Streaming Recurrent Neural Network Transducer—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