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 49014950 of 6433 papers

TitleStatusHype
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation0
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training0
Unsupervised domain adaptation for speech recognition with unsupervised error correction0
Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features0
Unsupervised Domain Adaptation Schemes for Building ASR in Low-resource Languages0
Unsupervised Domain Discovery using Latent Dirichlet Allocation for Acoustic Modelling in Speech Recognition0
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models0
Unsupervised Language agnostic WER Standardization0
Unsupervised Learning of Efficient and Robust Speech Representations0
Unsupervised Learning of Invariant Representations in Hierarchical Architectures0
Unsupervised Lexicon Discovery from Acoustic Input0
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges0
Unsupervised Method for Improving Arabic Speech Recognition Systems0
Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition0
Unsupervised Morphology-Based Vocabulary Expansion0
Unsupervised morph segmentation and statistical language models for vocabulary expansion0
Unsupervised Out-of-Distribution Dialect Detection with Mahalanobis Distance0
Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features0
Unsupervised pre-training for sequence to sequence speech recognition0
Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project0
Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project0
Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR0
Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR0
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition0
Unsupervised Speech Enhancement with speech recognition embedding and disentanglement losses0
Unsupervised Speech Recognition0
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching0
Unsupervised Stemming based Language Model for Telugu Broadcast News Transcription0
Unsupervised structured semantic inference for spoken dialog reservation tasks0
Unsupervised Text Normalization Using Distributed Representations of Words and Phrases0
Unsupervised training of neural mask-based beamforming0
Unsupervised Vocabulary Adaptation for Morph-based Language Models0
Unveiling Biases while Embracing Sustainability: Assessing the Dual Challenges of Automatic Speech Recognition Systems0
Unveiling the Role of Pretraining in Direct Speech Translation0
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models0
Up from Limited Dialog Systems!0
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?0
Use of GPU and Feature Reduction for Fast Query-by-Example Spoken Term Detection0
Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition0
Use of Machine Learning Technique to maximize the signal over background for H ττ0
Use of Speech Impairment Severity for Dysarthric Speech Recognition0
User Adaptive Restoration for Incorrectly-Segmented Utterances in Spoken Dialogue Systems0
User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis0
User Goal Change Model for Spoken Dialog State Tracking0
User-Initiated Repetition-Based Recovery in Multi-Utterance Dialogue Systems0
UserLibri: A Dataset for ASR Personalization Using Only Text0
以二維共振峰分布建立語者音色模型及其在語者驗證上之應用 (Using 2D Formant Distribution to Build Speaker Models and Its Application in Speaker Verification) [In Chinese]0
Using Ambiguity Detection to Streamline Linguistic Annotation0
Using an ASR database to design a pronunciation evaluation system in Basque0
Using an Ontology for Improved Automated Content Scoring of Spontaneous Non-Native Speech0
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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