SOTAVerified

Speech Recognition

Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.

( Image credit: SpecAugment )

Papers

Showing 16511700 of 6433 papers

TitleStatusHype
Decoupling recognition and transcription in Mandarin ASR0
Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition0
Audio-AdapterFusion: A Task-ID-free Approach for Efficient and Non-Destructive Multi-task Speech Recognition0
Deep Bayesian Natural Language Processing0
Data centric approach to Chinese Medical Speech Recognition0
Database Meets Deep Learning: Challenges and Opportunities0
DeepCon: An End-to-End Multilingual Toolkit for Automatic Minuting of Multi-Party Dialogues0
Deep context: end-to-end contextual speech recognition0
A Local Detection Approach for Named Entity Recognition and Mention Detection0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition0
Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition0
Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty0
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios0
Atypical Inputs in Educational Applications0
Almost Unsupervised Text to Speech and Automatic Speech Recognition0
Adaptable End-to-End ASR Models using Replaceable Internal LMs and Residual Softmax0
A Comparative Analysis of Crowdsourced Natural Language Corpora for Spoken Dialog Systems0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors0
Analysis of Data Augmentation Methods for Low-Resource Maltese ASR0
A two-step approach to leverage contextual data: speech recognition in air-traffic communications0
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey0
Deep Learning and Continuous Representations for Natural Language Processing0
Deep learning and face recognition: the state of the art0
Data Augmentation for Low-Resource Quechua ASR Improvement0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘190
A two-stage transliteration approach to improve performance of a multilingual ASR0
ParasNet: Fast Parasites Detection with Neural Networks0
Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition0
Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition0
Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition0
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data0
Data Augmentation for End-to-end Code-switching Speech Recognition0
Deep Learning for Computational Chemistry0
Deep Learning for Dialogue Systems0
Deep Learning for Distant Speech Recognition0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition0
Deep Learning for Forecasting Stock Returns in the Cross-Section0
Deep Learning for Lip Reading using Audio-Visual Information for Urdu Language0
Deep Learning for Pathological Speech: A Survey0
Deep Learning for Punctuation Restoration in Medical Reports0
Deep Learning for Single and Multi-Session i-Vector Speaker Recognition0
Deep Learning for Social Media Health Text Classification0
Deep Learning for Time-Series Analysis0
A Tutorial on Deep Neural Networks for Intelligent Systems0
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period0
Data and knowledge-driven approaches for multilingual training to improve the performance of speech recognition systems of Indian languages0
Deep Learning in the Automotive Industry: Applications and Tools0
AlloVera: A Multilingual Allophone Database0
Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified