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 45014550 of 6433 papers

TitleStatusHype
RadioTalk: a large-scale corpus of talk radio transcriptsCode0
Comparison of Neural Network Architectures for Spectrum Sensing0
Investigating Target Set Reduction for End-to-End Speech Recognition of Hindi-English Code-Switching Data0
Hierarchical Sequence to Sequence Voice Conversion with Limited Data0
Investigation on N-gram Approximated RNNLMs for Recognition of Morphologically Rich Speech0
Learn Spelling from Teachers: Transferring Knowledge from Language Models to Sequence-to-Sequence Speech Recognition0
PyKaldi2: Yet another speech toolkit based on Kaldi and PyTorchCode0
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition0
A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition0
Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition0
Multi-layer Attention Mechanism for Speech Keyword Recognition0
Teach an all-rounder with experts in different domains0
Joint Speech Recognition and Speaker Diarization via Sequence Transduction0
Transfer Learning from Audio-Visual Grounding to Speech Recognition0
M3D-GAN: Multi-Modal Multi-Domain Translation with Universal Attention0
Improving Reverberant Speech Training Using Diffuse Acoustic Simulation0
Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech RecognitionCode0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning0
NIESR: Nuisance Invariant End-to-end Speech RecognitionCode0
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training0
Toward Fairness in AI for People with Disabilities: A Research Roadmap0
End-to-End Speech Recognition with High-Frame-Rate Features Extraction0
Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic Speech Recognition0
Scalable Multi Corpora Neural Language Models for ASR0
Attention model for articulatory features detectionCode1
Kite: Automatic speech recognition for unmanned aerial vehicles0
Apport de l'adaptation automatique des mod\`eles de langage pour la reconnaissance de la parole: \'evaluation qualitative extrins\`eque dans un contexte de traitement de cours magistraux (Contribution of automatic adaptation of language models for speech recognition : extrinsic qualitative evaluation in a context of educational courses)0
Automated Cross-language Intelligibility Analysis of Parkinson's Disease Patients Using Speech Recognition Technologies0
An adaptable task-oriented dialog system for stand-alone embedded devices0
Deep Bayesian Natural Language Processing0
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization0
Improving Performance of End-to-End ASR on Numeric Sequences0
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR0
BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language RecognitionCode1
Analyzing Utility of Visual Context in Multimodal Speech Recognition Under Noisy Conditions0
Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion0
Essence Knowledge Distillation for Speech Recognition0
Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition0
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers0
A Winograd-based Integrated Photonics Accelerator for Convolutional Neural Networks0
Machine Learning Construction: implications to cybersecurity0
Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural NetworksCode0
Database Meets Deep Learning: Challenges and Opportunities0
Multi-Span Acoustic Modelling using Raw Waveform Signals0
Integration of TensorFlow based Acoustic Model with Kaldi WFST Decoder0
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models0
A simple and effective postprocessing method for image classification0
Code-Switching Detection Using ASR-Generated Language Posteriors0
Multi-Graph Decoding for Code-Switching ASR0
On combining features for single-channel robust speech recognition in reverberant environments0
Show:102550
← PrevPage 91 of 129Next →

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