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

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Showing 2351–2400 of 6433 papers

TitleStatusHype
Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation—0
SSCFormer: Push the Limit of Chunk-wise Conformer for Streaming ASR Using Sequentially Sampled Chunks and Chunked Causal Convolution—0
VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning—0
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale—0
Towards continually learning new languages—0
Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective—0
Exploring WavLM on Speech Enhancement—0
Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition—0
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer—0
Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review—0
On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches—0
Streaming Joint Speech Recognition and Disfluency DetectionCode0
L2 proficiency assessment using self-supervised speech representations—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder—0
Introducing Semantics into Speech Encoders—0
Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech RepresentationsCode0
FullPack: Full Vector Utilization for Sub-Byte Quantized Inference on General Purpose CPUs—0
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement—0
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation—0
Continuous Soft Pseudo-Labeling in ASR—0
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia ClassificationCode0
Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts—0
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding—0
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition—0
Adaptive Multi-Corpora Language Model Training for Speech Recognition—0
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition—0
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features—0
Robust Unstructured Knowledge Access in Conversational Dialogue with ASR ErrorsCode0
Streaming, fast and accurate on-device Inverse Text Normalization for Automatic Speech Recognition—0
End-to-End Evaluation of a Spoken Dialogue System for Learning Basic Mathematics—0
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR—0
LAMASSU: Streaming Language-Agnostic Multilingual Speech Recognition and Translation Using Neural Transducers—0
Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective—0
A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability—0
Stutter-TTS: Controlled Synthesis and Improved Recognition of Stuttered Speech—0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion—0
Minimum Latency Training of Sequence Transducers for Streaming End-to-End Speech Recognition—0
Biased Self-supervised learning for ASR—0
H_eval: A new hybrid evaluation metric for automatic speech recognition tasks—0
Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise—0
Adversarial Data Augmentation Using VAE-GAN for Disordered Speech Recognition—0
Phonetic-assisted Multi-Target Units Modeling for Improving Conformer-Transducer ASR system—0
Probing Statistical Representations For End-To-End ASR—0
Streaming Audio-Visual Speech Recognition with Alignment Regularization—0
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder—0
Variable Attention Masking for Configurable Transformer Transducer Speech Recognition—0
More Speaking or More Speakers?—0
Monolingual Recognizers Fusion for Code-switching Speech Recognition—0
Towards Zero-Shot Code-Switched Speech Recognition—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

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 BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage 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
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word 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