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 33513400 of 6433 papers

TitleStatusHype
The USTC-NEL Speech Translation system at IWSLT 20180
The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge0
The USTC-NERCSLIP Systems for the CHiME-8 NOTSOFAR-1 Challenge0
The USTC-NERCSLIP Systems for the CHiME-8 MMCSG Challenge0
The USTC-NERCSLIP Systems for The ICMC-ASR Challenge0
The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetes0
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation0
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge0
The WaveSurfer Automatic Speech Recognition Plugin0
The WAW Corpus: The First Corpus of Interpreted Speeches and their Translations for English and Arabic0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 20220
The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge0
The ZevoMOS entry to VoiceMOS Challenge 20220
Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition0
"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)0
Thoughts on the potential to compensate a hearing loss in noise0
HAINAN: Fast and Accurate Transducer for Hybrid-Autoregressive ASR0
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model0
Thutmose Tagger: Single-pass neural model for Inverse Text Normalization0
Tied Probabilistic Linear Discriminant Analysis for Speech Recognition0
Tigrinya Automatic Speech recognition with Morpheme based recognition units0
Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection0
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification0
Progressive Learning for Stabilizing Label Selection in Speech Separation with Mapping-based Method0
Time-Domain Speech Enhancement for Robust Automatic Speech Recognition0
Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism0
Time-Frequency Localization Using Deep Convolutional Maxout Neural Network in Persian Speech Recognition0
Time Series Classification using the Hidden-Unit Logistic Model0
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge0
TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices0
TLT-school: a Corpus of Non Native Children Speech0
T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events0
TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations0
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models0
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion0
Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments0
TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition0
Top-down training for neural networks0
Topic Classification on Spoken Documents Using Deep Acoustic and Linguistic Features0
Topic Identification and Discovery on Text and Speech0
Topic Identification for Speech without ASR0
Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts0
Topological Deep Learning for Speech Data0
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions0
To Reverse the Gradient or Not: An Empirical Comparison of Adversarial and Multi-task Learning in Speech Recognition0
TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch0
Tourist Guidance Robot Based on HyperCLOVA0
Toward Automated Content Feedback Generation for Non-native Spontaneous Speech0
Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations0
Toward a Web-based Speech Corpus for Algerian Dialectal Arabic Varieties0
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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