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 4901–4950 of 6433 papers

TitleStatusHype
Deep Audio-Visual Speech RecognitionCode1
Attention-based Audio-Visual Fusion for Robust Automatic Speech RecognitionCode1
Pre-training on high-resource speech recognition improves low-resource speech-to-text translationCode0
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition—0
Étude de l'informativité des transcriptions : une approche basée sur le résumé automatique—0
LRS3-TED: a large-scale dataset for visual speech recognitionCode0
Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural NetworksCode0
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial NetworksCode0
AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale—0
End-to-end Speech Recognition with Adaptive Computation Steps—0
Learning to adapt: a meta-learning approach for speaker adaptationCode0
Mean Field Analysis of Neural Networks: A Central Limit Theorem—0
Large Margin Neural Language Model—0
WiSeBE: Window-based Sentence Boundary EvaluationCode0
Augmenting Bottleneck Features of Deep Neural Network Employing Motor State for Speech Recognition at Humanoid Robots—0
Role of Intonation in Scoring Spoken English—0
Fast Spectrogram Inversion using Multi-head Convolutional Neural Networks—0
Linked Recurrent Neural Networks—0
Toward domain-invariant speech recognition via large scale training—0
Computing Word Classes Using Spectral Clustering—0
Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing—0
Neural Architecture Search: A SurveyCode0
Neural Collaborative Ranking—0
A Survey on Methods and Theories of Quantized Neural Networks—0
Densely Connected Convolutional Networks for Speech Recognition—0
End-to-end Speech Recognition with Word-based RNN Language Models—0
Dialog-context aware end-to-end speech recognition—0
Device-directed Utterance Detection—0
Deep context: end-to-end contextual speech recognition—0
GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware—0
Generalization Error in Deep Learning—0
Sequence Discriminative Training for Deep Learning based Acoustic Keyword Spotting—0
Linguistic Search Optimization for Deep Learning Based LVCSR—0
Mobile big data analysis with machine learning—0
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning—0
Learning with Noise-Contrastive Estimation: Easing training by learning to scale—0
Deep Bayesian Learning and Understanding—0
Indigenous language technologies in Canada: Assessment, challenges, and successes—0
On-Device Neural Language Model Based Word PredictionCode0
Deep Learning for Dialogue Systems—0
Joint Learning from Labeled and Unlabeled Data for Information Retrieval—0
Structured Dialogue Policy with Graph Neural Networks—0
Fast and Accurate Reordering with ITG Transition RNN—0
Word-Embedding based Content Features for Automated Oral Proficiency Scoring—0
Can spontaneous spoken language disfluencies help describe syntactic dependencies? An empirical study—0
Neural Network Architectures for Arabic Dialect Identification—0
A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer—0
Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge—0
Design of a Tigrinya Language Speech Corpus for Speech Recognition—0
Universal Approximation with Quadratic Deep Networks—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