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 40514100 of 6433 papers

TitleStatusHype
La transcription phon\'etique au bout des doigts, claviers et polices ergonomiques pour la transcription en API (Phonetic transcription at fingertips, ergonomics keyboards and fonts) [in French]0
Lattention: Lattice-attention in ASR rescoring0
Lattice-based Improvements for Voice Triggering Using Graph Neural Networks0
Lattice-based lightly-supervised acoustic model training0
Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers0
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models0
Lattice Rescoring for Speech Recognition using Large Scale Distributed Language Models0
Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition0
Lattice Transformer for Speech Translation0
Layer Pruning on Demand with Intermediate CTC0
Layer Reduction: Accelerating Conformer-Based Self-Supervised Model via Layer Consistency0
LDC Forced Aligner0
Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition0
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks0
Learn2Talk: 3D Talking Face Learns from 2D Talking Face0
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness0
Learned In Speech Recognition: Contextual Acoustic Word Embeddings0
Learned in Speech Recognition: Contextual Acoustic Word Embeddings0
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition0
LearnerVoice: A Dataset of Non-Native English Learners' Spontaneous Speech0
Learning a Deep Hybrid Model for Semi-Supervised Text Classification0
Learning a Dual-Mode Speech Recognition Model via Self-Pruning0
Learning a Neural Diff for Speech Models0
Learning ASR pathways: A sparse multilingual ASR model0
Learning-based A Posteriori Speech Presence Probability Estimation and Applications0
Learning-Based Data Storage [Vision] (Technical Report)0
Learning Compact Recurrent Neural Networks0
Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition0
Learning Cross-lingual Mappings for Data Augmentation to Improve Low-Resource Speech Recognition0
Learning Domain-Independent Dialogue Policies via Ontology Parameterisation0
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation0
Learning Ensembles of Structured Prediction Rules0
Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion0
Learning from 26 Languages: Program Management and Science in the Babel Program0
Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition0
Learning From the Master: Distilling Cross-Modal Advanced Knowledge for Lip Reading0
Learning Hard Alignments with Variational Inference0
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models0
Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation0
Learning Invariant Representation and Risk Minimized for Unsupervised Accent Domain Adaptation0
Learning Joint Acoustic-Phonetic Word Embeddings0
Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition0
Learning Language Structures through Grounding0
Learning linearly separable features for speech recognition using convolutional neural networks0
Learning Monotonic Alignments with Source-Aware GMM Attention0
Learning Multiscale Features Directly From Waveforms0
Learning neural trans-dimensional random field language models with noise-contrastive estimation0
Learning Noise-Invariant Representations for Robust Speech Recognition0
Learning Noise-Invariant Representations for Robust Speech Recognition0
Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition0
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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