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 301350 of 6433 papers

TitleStatusHype
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy SpeechCode1
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech RecognitionCode1
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMICode1
Continuous speech separation: dataset and analysisCode1
Compiling ONNX Neural Network Models Using MLIRCode1
Comparative layer-wise analysis of self-supervised speech modelsCode1
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech RecognitionCode1
Common Voice: A Massively-Multilingual Speech CorpusCode1
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global ContextCode1
BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language RecognitionCode1
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation ModelsCode1
Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT)Code1
Communication-Efficient Learning of Deep Networks from Decentralized DataCode1
Computer-Generated Music for Tabletop Role-Playing GamesCode1
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple LanguagesCode1
D4AM: A General Denoising Framework for Downstream Acoustic ModelsCode1
Cross Attention Augmented Transducer Networks for Simultaneous TranslationCode1
Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech RecognitionCode1
CLSRIL-23: Cross Lingual Speech Representations for Indic LanguagesCode1
AISHELL-NER: Named Entity Recognition from Chinese SpeechCode1
CL-MASR: A Continual Learning Benchmark for Multilingual ASRCode1
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
Accented Speech Recognition With Accent-specific CodebooksCode1
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network LatencyCode1
Deep Learning Enabled Semantic Communications with Speech Recognition and SynthesisCode1
Deep Sparse Conformer for Speech RecognitionCode1
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applicationsCode1
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact CentersCode1
CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing ModelsCode1
A Fully Differentiable Beam Search DecoderCode1
A Further Study of Unsupervised Pre-training for Transformer Based Speech RecognitionCode1
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command RecognitionCode1
CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command RecognitionCode1
Discriminative Multi-modality Speech RecognitionCode1
CIF: Continuous Integrate-and-Fire for End-to-End Speech RecognitionCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
DistilXLSR: A Light Weight Cross-Lingual Speech Representation ModelCode1
dMel: Speech Tokenization made SimpleCode1
Dompteur: Taming Audio Adversarial ExamplesCode1
DOVER: A Method for Combining Diarization OutputsCode1
Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech TranslationCode1
Dual-Path Style Learning for End-to-End Noise-Robust Speech RecognitionCode1
CAPE: Encoding Relative Positions with Continuous Augmented Positional EmbeddingsCode1
CB-Conformer: Contextual biasing Conformer for biased word recognitionCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
A Discriminative Hierarchical PLDA-based Model for Spoken Language RecognitionCode1
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and BetterCode1
Efficiently Modeling Long Sequences with Structured State SpacesCode1
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and LanguageCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
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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