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 351400 of 6433 papers

TitleStatusHype
Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language TextCode1
CLSRIL-23: Cross Lingual Speech Representations for Indic LanguagesCode1
A Hybrid Continuity Loss to Reduce Over-Suppression for Time-domain Target Speaker ExtractionCode1
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple LanguagesCode1
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command RecognitionCode1
AI Accelerator Survey and TrendsCode1
CIF: Continuous Integrate-and-Fire for End-to-End Speech RecognitionCode1
Attention model for articulatory features detectionCode1
FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph TransformerCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing ModelsCode1
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech RecognitionCode1
Jointly Learning Visual and Auditory Speech Representations from Raw DataCode1
FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural NetworksCode1
AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition BaselineCode1
A Cross-Modal Approach to Silent Speech with LLM-Enhanced RecognitionCode1
AISHELL-NER: Named Entity Recognition from Chinese SpeechCode1
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition BaselineCode1
FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited KnowledgeCode1
Compiling ONNX Neural Network Models Using MLIRCode1
Computer-Generated Music for Tabletop Role-Playing GamesCode1
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech RecognitionCode1
Kosp2e: Korean Speech to English Translation CorpusCode1
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech RecognitionCode1
FedScale: Benchmarking Model and System Performance of Federated Learning at ScaleCode1
Attention-based Audio-Visual Fusion for Robust Automatic Speech RecognitionCode1
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMICode1
Language and Speech Technology for Central Kurdish VarietiesCode1
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation ScoringCode1
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy SpeechCode1
Advancing Test-Time Adaptation in Wild Acoustic Test SettingsCode1
BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language RecognitionCode1
Attack on practical speaker verification system using universal adversarial perturbationsCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithmCode1
A context-aware knowledge transferring strategy for CTC-based ASRCode1
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control CommunicationsCode1
CoVoST 2 and Massively Multilingual Speech-to-Text TranslationCode1
Cross Attention Augmented Transducer Networks for Simultaneous TranslationCode1
A transfer learning based approach for pronunciation scoringCode1
Attention-Based Models for Speech RecognitionCode1
Radically Old Way of Computing Spectra: Applications in End-to-End ASRCode1
CTC-synchronous Training for Monotonic Attention ModelCode1
AlignVSR: Audio-Visual Cross-Modal Alignment for Visual Speech RecognitionCode1
DARF: A data-reduced FADE version for simulations of speech recognition thresholds with real hearing aidsCode1
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across ModalitiesCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
Learning to Detect Noisy Labels Using Model-Based FeaturesCode1
A^3T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and EditingCode1
FlanEC: Exploring Flan-T5 for Post-ASR Error CorrectionCode1
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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
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord 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