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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 2801–2850 of 3012 papers

TitleStatusHype
Modelling prosodic structure using Artificial Neural Networks—0
End-to-end neural networks for subvocal speech recognition—0
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LMCode0
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments—0
ASR error management for improving spoken language understanding—0
Anti-spoofing Methods for Automatic SpeakerVerification System—0
Local Monotonic Attention Mechanism for End-to-End Speech and Language Processing—0
Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition—0
A Generative Model of a Pronunciation Lexicon for Hindi—0
M\'alr\'omur: A Manually Verified Corpus of Recorded Icelandic Speech—0
Speech-Based Visual Question AnsweringCode0
Acoustic Model Compression with MAP adaptation—0
Towards Estimating the Upper Bound of Visual-Speech Recognition: The Visual Lip-Reading Feasibility Database—0
Automatic Viseme Vocabulary Construction to Enhance Continuous Lip-reading—0
The SUMMA Platform Prototype—0
A Hierarchical Neural Model for Learning Sequences of Dialogue Acts—0
Real-Time Keyword Extraction from Conversations—0
``Oh, I've Heard That Before'': Modelling Own-Dialect Bias After Perceptual Learning by Weighting Training Data—0
An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems—0
Gender and Dialect Bias in YouTube's Automatic Captions—0
Identifying dialects with textual and acoustic cues—0
An enhanced automatic speech recognition system for Arabic—0
A Code-Switching Corpus of Turkish-German Conversations—0
CASSANDRA: A multipurpose configurable voice-enabled human-computer-interface—0
Learning Similarity Functions for Pronunciation Variations—0
Topic Identification for Speech without ASR—0
Direct Acoustics-to-Word Models for English Conversational Speech Recognition—0
Recognizing Multi-talker Speech with Permutation Invariant Training—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
Towards End-to-End Speech Recognition with Deep Convolutional Neural NetworksCode0
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition—0
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
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
Automatic Speech Recognition Errors as a Predictor of L2 Listening Difficulties—0
Audio Segmentation for Robust Real-Time Speech Recognition Based on Neural NetworksCode0
Automatic Syllabification for Manipuri language—0
Lightly Supervised Quality Estimation—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