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
The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese0
The CAPIO 2017 Conversational Speech Recognition System0
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System0
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios0
The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization0
The coding and annotation of multimodal dialogue acts0
The Cohort and Speechify Libraries for Rapid Construction of Speech Enabled Applications for Android0
The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge0
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning0
The Dialog State Tracking Challenge0
The DIRHA Portuguese Corpus: A Comparison of Home Automation Command Detection and Recognition in Simulated and Real Data.0
The DIRHA simulated corpus0
The DISCO ASR-based CALL system: practicing L2 oral skills and beyond0
The EASR Corpora of European Portuguese, French, Hungarian and Polish Elderly Speech0
The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR0
The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition0
The Effect of Cognitive Load on a Statistical Dialogue System0
The Effect of Dependency Representation Scheme on Syntactic Language Modelling0
The Effect of Sensor Errors in Situated Human-Computer Dialogue0
The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages0
The ETAPE corpus for the evaluation of speech-based TV content processing in the French language0
The ETAPE speech processing evaluation0
The evaluation of a code-switched Sepedi-English automatic speech recognition system0
The Faetar Benchmark: Speech Recognition in a Very Under-Resourced Language0
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems0
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines0
The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models0
The Future of Spoken Dialogue Systems is in their Past: Long-Term Adaptive, Conversational Assistants0
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning0
The GUA-Speech System Description for CNVSRC Challenge 20230
The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis0
The Herme Database of Spontaneous Multimodal Human-Robot Dialogues0
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches0
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation0
The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation0
The IBM 2015 English Conversational Telephone Speech Recognition System0
The IBM 2016 English Conversational Telephone Speech Recognition System0
The IBM 2016 Speaker Recognition System0
The IBM Speaker Recognition System: Recent Advances and Error Analysis0
The ILMT-s2s Corpus ― A Multimodal Interlingual Map Task Corpus0
The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR0
The Importance of Recommender and Feedback Features in a Pronunciation Learning Aid0
The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software0
The InproTK 2012 release0
The IOIT English ASR system for IWSLT 20160
The IWSLT 2011 Evaluation Campaign on Automatic Talk Translation0
The IWSLT 2016 Evaluation Campaign0
The IWSLT 2019 KIT Speech Translation System0
The IWSLT 2021 BUT Speech Translation Systems0
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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