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 551–600 of 6433 papers

TitleStatusHype
A Comprehensive Survey on Graph Neural NetworksCode1
Compiling ONNX Neural Network Models Using MLIRCode1
Attention-Based Models for Speech RecognitionCode1
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech RecognitionCode1
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global ContextCode1
BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language RecognitionCode1
Continuous speech separation: dataset and analysisCode1
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple LanguagesCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
Attention model for articulatory features detectionCode1
Audio-Visual Representation Learning via Knowledge Distillation from Speech Foundation ModelsCode1
Automatic Speech Recognition for Speech Assessment of Persian Preschool ChildrenCode1
Cross-Speaker Encoding Network for Multi-Talker Speech RecognitionCode1
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control CommunicationsCode1
D4AM: A General Denoising Framework for Downstream Acoustic ModelsCode1
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across ModalitiesCode1
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network LatencyCode1
Deep Contextualized Acoustic Representations For Semi-Supervised Speech RecognitionCode1
A Systematic Comparison of Phonetic Aware Techniques for Speech EnhancementCode1
Deep Speech: Scaling up end-to-end speech recognitionCode1
A transfer learning based approach for pronunciation scoringCode1
DENT-DDSP: Data-efficient noisy speech generator using differentiable digital signal processors for explicit distortion modelling and noise-robust speech recognitionCode1
Discriminative Multi-modality Speech RecognitionCode1
Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTCCode1
Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence ModelingCode1
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech RecognitionCode1
An exact mapping between the Variational Renormalization Group and Deep LearningCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
dMel: Speech Tokenization made SimpleCode1
Attack on practical speaker verification system using universal adversarial perturbationsCode1
DOVER: A Method for Combining Diarization OutputsCode1
Do VSR Models Generalize Beyond LRS3?Code1
Advancing Test-Time Adaptation in Wild Acoustic Test SettingsCode1
A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text CorporaCode1
DuplexMamba: Enhancing Real-time Speech Conversations with Duplex and Streaming CapabilitiesCode1
DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic ModelCode1
Earnings-22: A Practical Benchmark for Accents in the WildCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and LanguageCode1
ASR2K: Speech Recognition for Around 2000 Languages without AudioCode1
EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation LearningCode1
Emotion Recognition from Speech Using Wav2vec 2.0 EmbeddingsCode1
Emotion Recognition in Audio and Video Using Deep Neural NetworksCode1
EnCodecMAE: Leveraging neural codecs for universal audio representation learningCode1
End-to-end Audio-visual Speech Recognition with ConformersCode1
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applicationsCode1
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN MMIPercentage error12.9—Unverified
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN BMMIPercentage error12.9—Unverified
9DNN MPEPercentage error12.9—Unverified
10Deep Speech + FSHPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
5Deep Speech 2Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified