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 17011750 of 6433 papers

TitleStatusHype
Deep Learning Scaling is Predictable, Empirically0
Deep Learning to Address Candidate Generation and Cold Start Challenges in Recommender Systems: A Research Survey0
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings0
Deep Learning Works in Practice. But Does it Work in Theory?0
Deep Lip Reading: a comparison of models and an online application0
Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition0
Deep LSTM based Feature Mapping for Query Classification0
Deep LSTM for Large Vocabulary Continuous Speech Recognition0
Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer0
Deep Multimodal Learning for Audio-Visual Speech Recognition0
ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters0
Deep Network Guided Proof Search0
Deep Neural Network Approximation Theory0
Deep Neural Network Language Models0
Deep Neural Networks0
Deep Neural Networks for Acoustic Modeling in Speech Recognition0
Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data0
Deep Photonic Reservoir Computer for Speech Recognition0
Click or Type: An Analysis of Wizard's Interaction for Future Wizard Interface Design0
Are E2E ASR models ready for an industrial usage?0
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends0
Deep Reservoir Computing Using Cellular Automata0
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks0
Deep Shallow Fusion for RNN-T Personalization0
Clean Label Attacks against SLU Systems0
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers0
Are disentangled representations all you need to build speaker anonymization systems?0
Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks0
Class LM and word mapping for contextual biasing in End-to-End ASR0
Automated Preamble Detection in Dictated Medical Reports0
Deep Spoken Keyword Spotting: An Overview0
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization0
Classist Tools: Social Class Correlates with Performance in NLP0
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models0
Deep Visual Forced Alignment: Learning to Align Transcription with Talking Face Video0
Automated Speaker Independent Visual Speech Recognition: A Comprehensive Survey0
Deep Xi as a Front-End for Robust Automatic Speech Recognition0
Defense against Adversarial Attacks on Hybrid Speech Recognition using Joint Adversarial Fine-tuning with Denoiser0
Deformable TDNN with adaptive receptive fields for speech recognition0
DEFORMER: Coupling Deformed Localized Patterns with Global Context for Robust End-to-end Speech Recognition0
Delayed Fusion: Integrating Large Language Models into First-Pass Decoding in End-to-end Speech Recognition0
Automated speech tools for helping communities process restricted-access corpora for language revival efforts0
A Recorded Debating Dataset0
Deliberation Model Based Two-Pass End-to-End Speech Recognition0
Deliberation Model for On-Device Spoken Language Understanding0
Automatically Assess Children's Reading Skills0
Delta Networks for Optimized Recurrent Network Computation0
Automatically building a Tunisian Lexicon for Deverbal Nouns0
Adversarial Training for Multilingual Acoustic Modeling0
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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