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 62016250 of 6433 papers

TitleStatusHype
CNN architecture extraction on edge GPU0
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments0
CNVSRC 2023: The First Chinese Continuous Visual Speech Recognition Challenge0
CNVSRC 2024: The Second Chinese Continuous Visual Speech Recognition Challenge0
CoALT: A Software for Comparing Automatic Labelling Tools0
Coarse-To-Fine And Cross-Lingual ASR Transfer0
CobaltF: A Fluent Metric for MT Evaluation0
CoBiLiRo: A Research Platform for Bimodal Corpora0
Cocktail-Party Audio-Visual Speech Recognition0
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations0
CoDERT: Distilling Encoder Representations with Co-learning for Transducer-based Speech Recognition0
Code Switched and Code Mixed Speech Recognition for Indic languages0
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences0
Code-Switching Detection Using ASR-Generated Language Posteriors0
Codeswitching Detection via Lexical Features in Conditional Random Fields0
Code-Switching Detection with Data-Augmented Acoustic and Language Models0
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition0
Code-Switching Text Generation and Injection in Mandarin-English ASR0
Code-Switching without Switching: Language Agnostic End-to-End Speech Translation0
Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition0
Cold Fusion: Training Seq2Seq Models Together with Language Models0
Co-learning synaptic delays, weights and adaptation in spiking neural networks0
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants0
Collaborative Learning with Artificial Intelligence Speakers (CLAIS): Pre-Service Elementary Science Teachers' Responses to the Prototype0
Collaborative Training of Acoustic Encoders for Speech Recognition0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders0
Collecting Code-Switched Data from Social Media0
Collection and Analysis of Code-switch Egyptian Arabic-English Speech Corpus0
Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling0
Combining Human Inputters and Language Services to provide Multi-language support system for International Symposiums0
Combining Language Models For Specialized Domains: A Colorful Approach0
Combining Multiple Views for Visual Speech Recognition0
Combining Natural Gradient with Hessian Free Methods for Sequence Training0
Combining Open Source Annotators for Entity Linking through Weighted Voting0
Combining Punctuation and Disfluency Prediction: An Empirical Study0
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation0
Combining Unsupervised and Text Augmented Semi-Supervised Learning for Low Resourced Autoregressive Speech Recognition0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition0
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition0
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks0
Communication strategies for a computerized caregiver for individuals with Alzheimer's disease0
Compact, Efficient and Unlimited Capacity: Language Modeling with Compressed Suffix Trees0
Compacting Neural Network Classifiers via Dropout Training0
Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness0
Comparative Analysis of Polynomial and Rational Approximations of Hyperbolic Tangent Function for VLSI Implementation0
Comparative Analysis of the wav2vec 2.0 Feature Extractor0
Comparative Error Analysis of Dialog State Tracking0
Comparing and combining classifiers for self-taught vocal interfaces0
Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task0
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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