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 1401–1450 of 6433 papers

TitleStatusHype
Neural approaches to spoken content embedding—0
The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge—0
Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech TasksCode0
Decoupled Structure for Improved Adaptability of End-to-End Models—0
A Small and Fast BERT for Chinese Medical Punctuation RestorationCode0
Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion—0
AdVerb: Visually Guided Audio Dereverberation—0
KinSPEAK: Improving speech recognition for Kinyarwanda via semi-supervised learning methods—0
Convoifilter: A case study of doing cocktail party speech recognition—0
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model—0
An Effective Transformer-based Contextual Model and Temporal Gate Pooling for Speaker IdentificationCode0
TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition—0
Improving Continuous Sign Language Recognition with Cross-Lingual Signs—0
Another Point of View on Visual Speech Recognition—0
Indonesian Automatic Speech Recognition with XLSR-53—0
Bayes Risk Transducer: Transducer with Controllable Alignment Prediction—0
Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals—0
Accurate synthesis of Dysarthric Speech for ASR data augmentation—0
End-to-End Open Vocabulary Keyword Search With Multilingual Neural RepresentationsCode0
Improving CTC-AED model with integrated-CTC and auxiliary loss regularization—0
AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model—0
O-1: Self-training with Oracle and 1-best Hypothesis—0
Using Text Injection to Improve Recognition of Personal Identifiers in Speech—0
Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion EncoderCode1
Text Injection for Capitalization and Turn-Taking Prediction in Speech Models—0
Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for ConversationsCode0
Cross-Attribute Matrix Factorization Model with Shared User Embedding—0
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition—0
Bilingual Streaming ASR with Grapheme units and Auxiliary Monolingual Loss—0
Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping—0
Improving Joint Speech-Text Representations Without Alignment—0
A Novel Self-training Approach for Low-resource Speech Recognition—0
Conformer-based Target-Speaker Automatic Speech Recognition for Single-Channel AudioCode0
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks—0
TSSR: A Truncated and Signed Square Root Activation Function for Neural Networks—0
Unsupervised Out-of-Distribution Dialect Detection with Mahalanobis Distance—0
A Novel Method for improving accuracy in neural network by reinstating traditional back propagation technique—0
OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data GenerationCode1
Comparative Analysis of the wav2vec 2.0 Feature Extractor—0
On Monotonic Aggregation for Open-domain QACode0
Dialogue Systems Can Generate Appropriate Responses without the Use of Question Marks? -- Investigation of the Effects of Question Marks on Dialogue Systems—0
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism—0
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems—0
ApproBiVT: Lead ASR Models to Generalize Better Using Approximated Bias-Variance Tradeoff Guided Early Stopping and Checkpoint Averaging—0
Speaker Diarization of Scripted Audiovisual Content—0
Federated Representation Learning for Automatic Speech Recognition—0
Careful Whisper -- leveraging advances in automatic speech recognition for robust and interpretable aphasia subtype classification—0
Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings—0
Mispronunciation detection using self-supervised speech representationsCode0
Pre-training End-to-end ASR Models with Augmented Speech Samples Queried by Text—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