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.

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Papers

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Showing 2751–2800 of 6433 papers

TitleStatusHype
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm—0
HENT-SRT: Hierarchical Efficient Neural Transducer with Self-Distillation for Joint Speech Recognition and Translation—0
HESITA(te) in Portuguese—0
Heterogeneous Language Model Optimization in Automatic Speech Recognition—0
Heterogeneous Reservoir Computing Models for Persian Speech Recognition—0
Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review—0
Hierarchical Bayesian Language Modelling for the Linguistically Informed—0
Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units—0
Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR—0
Hierarchical Latent Words Language Models for Robust Modeling to Out-Of Domain Tasks—0
Hierarchical Multitask Learning for CTC-based Speech Recognition—0
Hierarchical Multi Task Learning With CTC—0
Hierarchical Neural Network Architecture In Keyword Spotting—0
Hierarchical Recurrent Adapters for Efficient Multi-Task Adaptation of Large Speech Models—0
Chipmunk: A Systolically Scalable 0.9 mm^2, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference—0
Hierarchical Sequence to Sequence Voice Conversion with Limited Data—0
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation—0
Hierarchical Summarization for Longform Spoken Dialog—0
Fast offline Transformer-based end-to-end automatic speech recognition for real-world applications—0
Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation—0
Chinese Medical Speech Recognition with Punctuated Hypothesis—0
High-Accuracy and Low-Latency Speech Recognition with Two-Head Contextual Layer Trajectory LSTM Model—0
Highland Puebla Nahuatl Speech Translation Corpus for Endangered Language Documentation—0
Highly-Reverberant Real Environment database: HRRE—0
Arabic Diacritization with Recurrent Neural Networks—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
Chinese-LiPS: A Chinese audio-visual speech recognition dataset with Lip-reading and Presentation Slides—0
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR—0
Fast Labeling and Transcription with the Speechalyzer Toolkit—0
Highway Long Short-Term Memory RNNs for Distant Speech Recognition—0
Hindi-English Code-Switching Speech Corpus—0
HinMA: Distributed Morphology based Hindi Morphological Analyzer—0
FastInject: Injecting Unpaired Text Data into CTC-based ASR training—0
CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings—0
Arabic Code-Switching Speech Recognition using Monolingual Data—0
HMM-Free Encoder Pre-Training for Streaming RNN Transducer—0
Adversarial Speaker Adaptation—0
homeService: Voice-enabled assistive technology in the home using cloud-based automatic speech recognition—0
Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly Speaker Adaptation—0
Homophone-based Label Smoothing in End-to-End Automatic Speech Recognition—0
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network—0
Child Speech Recognition in Human-Robot Interaction: Problem Solved?—0
Houdini: Fooling Deep Structured Prediction Models—0
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples—0
Fast frequency modulation is encoded according to the listener expectations in the human subcortical auditory pathway—0
Faster Speech-LLaMA Inference with Multi-token Prediction—0
How Accents Confound: Probing for Accent Information in End-to-End Speech Recognition Systems—0
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR—0
How Diachronic Text Corpora Affect Context based Retrieval of OOV Proper Names for Audio News—0
Children's Speech Recognition through Discrete Token Enhancement—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