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 54515500 of 6433 papers

TitleStatusHype
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip0
Sparse Transcription0
SparseVSR: Lightweight and Noise Robust Visual Speech Recognition0
Sparsification via Compressed Sensing for Automatic Speech Recognition0
Sparsifying Networks via Subdifferential Inclusion0
Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity0
Spatial Audio Processing with Large Language Model on Wearable Devices0
Spatial Correlation and Value Prediction in Convolutional Neural Networks0
Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments0
Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading0
Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading0
Speaker Adaptation for Attention-Based End-to-End Speech Recognition0
Speaker Adaptation for End-to-End CTC Models0
Speaker adaptation for Wav2vec2 based dysarthric ASR0
Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition0
Speaker Adapted Beamforming for Multi-Channel Automatic Speech Recognition0
Speaker-Adapted End-to-End Visual Speech Recognition for Continuous Spanish0
Speaker- and Age-Invariant Training for Child Acoustic Modeling Using Adversarial Multi-Task Learning0
Speaker and Language Change Detection using Wav2vec2 and Whisper0
Speaker Anonymization with Phonetic Intermediate Representations0
Speaker-aware speech-transformer0
Speaker Change Detection for Transformer Transducer ASR0
Speaker Cluster-Based Speaker Adaptive Training for Deep Neural Network Acoustic Modeling0
Speaker conditioning of acoustic models using affine transformation for multi-speaker speech recognition0
Speaker conditioned acoustic modeling for multi-speaker conversational ASR0
Speaker Diarization of Scripted Audiovisual Content0
Speaker Diarization with Lexical Information0
Speaker-Distinguishable CTC: Learning Speaker Distinction Using CTC for Multi-Talker Speech Recognition0
Speaker Identification using Speech Recognition0
Speaker-Independent Speech-Driven Visual Speech Synthesis using Domain-Adapted Acoustic Models0
Speaker Mask Transformer for Multi-talker Overlapped Speech Recognition0
Speaker Recognition in Bengali Language from Nonlinear Features0
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition0
Speaker Selective Beamformer with Keyword Mask Estimation0
Speaker Separation Using Speaker Inventories and Estimated Speech0
Speaker Tagging Correction With Non-Autoregressive Language Models0
Speaker-Targeted Audio-Visual Models for Speech Recognition in Cocktail-Party Environments0
Speak & Improve Challenge 2025: Tasks and Baseline Systems0
Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback0
SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning0
SpecAugment on Large Scale Datasets0
Spectral decomposition method of dialog state tracking via collective matrix factorization0
Spectral Dependency Parsing with Latent Variables0
Spectral feature mapping with mimic loss for robust speech recognition0
Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech0
Spectral modification for recognition of children’s speech undermismatched conditions0
Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition0
Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models0
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech0
Speech and language technologies for the automatic monitoring and training of cognitive functions0
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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