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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 2826–2850 of 3012 papers

TitleStatusHype
Topic Identification for Speech without ASR—0
Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks—0
DECCA Repurposed: Detecting transcription inconsistencies without an orthographic standard—0
Residual Convolutional CTC Networks for Automatic Speech Recognition—0
On the Relevance of Auditory-Based Gabor Features for Deep Learning in Automatic Speech Recognition—0
Towards speech-to-text translation without speech recognition—0
Structural Analysis of Hindi Phonetics and A Method for Extraction of Phonetically Rich Sentences from a Very Large Hindi Text Corpus—0
Learning Word-Like Units from Joint Audio-Visual Analysis—0
Lyrics-to-Audio Alignment by Unsupervised Discovery of Repetitive Patterns in Vowel Acoustics—0
Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading—0
End-to-End ASR-free Keyword Search from Speech—0
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition—0
Towards End-to-End Speech Recognition with Deep Convolutional Neural NetworksCode0
Evaluating Low-Level Speech Features Against Human Perceptual Data—0
Recurrent Deep Stacking Networks for Speech Recognition—0
Incorporating Language Level Information into Acoustic Models—0
Evaluating Automatic Speech Recognition Systems in Comparison With Human Perception Results Using Distinctive Feature Measures—0
Towards better decoding and language model integration in sequence to sequence models—0
Audio Segmentation for Robust Real-Time Speech Recognition Based on Neural NetworksCode0
The IWSLT 2016 Evaluation Campaign—0
RACAI Entry for the IWSLT 2016 Shared Task—0
使用字典學習法於強健性語音辨識 (The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]—0
Bayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words—0
Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network—0
Automatic Syllabification for Manipuri language—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