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 45514600 of 6433 papers

TitleStatusHype
The JHU Multi-Microphone Multi-Speaker ASR System for the CHiME-6 Challenge0
The KIT Lecture Corpus for Speech Translation0
The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities0
The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition0
The Map Task Dialogue System: A Test-bed for Modelling Human-Like Dialogue0
The Marchex 2018 English Conversational Telephone Speech Recognition System0
The MeMAD Submission to the IWSLT 2018 Speech Translation Task0
The META-SHARE Language Resources Sharing Infrastructure: Principles, Challenges, Solutions0
The Microsoft 2016 Conversational Speech Recognition System0
The Microsoft 2017 Conversational Speech Recognition System0
The Microsoft Speech Language Translation (MSLT) Corpus for Chinese and Japanese: Conversational Test data for Machine Translation and Speech Recognition0
The Multicultural Medical Assistant: Can LLMs Improve Medical ASR Errors Across Borders?0
The Multilingual TEDx Corpus for Speech Recognition and Translation0
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction0
The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition0
The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages0
The News Delivery Channel Recommendation Based on Granular Neural Network0
The NIGENS General Sound Events Database0
The Nijmegen Corpus of Casual Czech0
The North System for Formosa Speech Recognition Challenge 20230
The Norwegian Parliamentary Speech Corpus0
The NPU-ASLP System for Audio-Visual Speech Recognition in MISP 2022 Challenge0
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 20200
The NUS-HLT System for ICASSP2024 ICMC-ASR Grand Challenge0
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities0
The OpenGrm open-source finite-state grammar software libraries0
The PARLANCE mobile application for interactive search in English and Mandarin0
The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition0
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage0
The Perceptimatic English Benchmark for Speech Perception Models0
The Role of Phonetic Units in Speech Emotion Recognition0
The RoyalFlush System of Speech Recognition for M2MeT Challenge0
The RWTH Aachen LVCSR system for IWSLT-2016 German Skype conversation recognition task0
The SAFE-T Corpus: A New Resource for Simulated Public Safety Communications0
The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in Conversational Environments0
The simpler the better: vanilla sgd revisited0
The SI TEDx-UM speech database: a new Slovenian Spoken Language Resource0
The SJTU System for Dialog State Tracking Challenge 20
The Slovene BNSI Broadcast News database and reference speech corpus GOS: Towards the uniform guidelines for future work0
The Sogou-TIIC Speech Translation System for IWSLT 20180
The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models0
The "Sound of Silence" in EEG -- Cognitive voice activity detection0
The Spatial Selective Auditory Attention of Cochlear Implant Users in Different Conversational Sound Levels0
The Speechtransformer for Large-scale Mandarin Chinese Speech Recognition0
The State of Commercial Automatic French Legal Speech Recognition Systems and their Impact on Court Reporters et al0
The SUMMA Platform Prototype0
The Sweet-Home speech and multimodal corpus for home automation interaction0
The Tag-Team Approach: Leveraging CLS and Language Tagging for Enhancing Multilingual ASR0
The THUEE System Description for the IARPA OpenASR21 Challenge0
The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems0
Show:102550
← PrevPage 92 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