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 16011650 of 6433 papers

TitleStatusHype
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition0
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition0
Deep Multimodal Representation Learning from Temporal Data0
Deep Neural Network Approximation Theory0
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation0
DASB -- Discrete Audio and Speech Benchmark0
Deep Recurrent Neural Networks for Acoustic Modelling0
Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition0
Data Augmentation for End-to-end Code-switching Speech Recognition0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘190
Data Augmentation for Low-Resource Quechua ASR Improvement0
Analysis of Data Augmentation Methods for Low-Resource Maltese ASR0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors0
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios0
Atypical Inputs in Educational Applications0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer0
Deploying Technology to Save Endangered Languages0
Data centric approach to Chinese Medical Speech Recognition0
Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition0
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning0
Audio Adversarial Examples for Robust Hybrid CTC/Attention Speech Recognition0
DataMix: Efficient Privacy-Preserving Edge-Cloud Inference0
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement0
Data Selection With Fewer Words0
Data-selective Transfer Learning for Multi-Domain Speech Recognition0
Cloud-Based Face and Speech Recognition for Access Control Applications0
Data Techniques For Online End-to-end Speech Recognition0
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting0
DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions0
DCIM-AVSR : Efficient Audio-Visual Speech Recognition via Dual Conformer Interaction Module0
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR0
Debiased Automatic Speech Recognition for Dysarthric Speech via Sample Reweighting with Sample Affinity Test0
DECCA Repurposed: Detecting transcription inconsistencies without an orthographic standard0
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples0
A Research Platform for Multi-Robot Dialogue with Humans0
Decipherment0
Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages0
DECODA: a call-centre human-human spoken conversation corpus0
AudioFool: Fast, Universal and synchronization-free Cross-Domain Attack on Speech Recognition0
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation0
Decoder-only Architecture for Streaming End-to-end Speech Recognition0
Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable0
Decoding visemes: improving machine lipreading0
Decoding with Finite-State Transducers on GPUs0
Decoupled Federated Learning for ASR with Non-IID Data0
Decoupled Structure for Improved Adaptability of End-to-End Models0
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions0
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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