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 27012750 of 6433 papers

TitleStatusHype
Mesures linguistiques automatiques pour l’évaluation des systèmes de Reconnaissance Automatique de la Parole (Automated linguistic measures for automatic speech recognition systems’ evaluation)0
A Systematic Approach to Derive a Refined Speech Corpus for Sinhala0
Conversational Speech Recognition Needs Data? Experiments with Austrian German0
Post-Stroke Speech Transcription Challenge (Task B): Correctness Detection in Anomia Diagnosis with Imperfect Transcripts0
RUSAVIC Corpus: Russian Audio-Visual Speech in Cars0
Samrómur Children: An Icelandic Speech Corpus0
Handwriting recognition for Scottish Gaelic0
Standard German Subtitling of Swiss German TV content: the PASSAGE Project0
Towards an Open-Source Dutch Speech Recognition System for the Healthcare Domain0
Huqariq: A Multilingual Speech Corpus of Native Languages of Peru forSpeech Recognition0
A Semi-Automated Live Interlingual Communication Workflow Featuring Intralingual Respeaking: Evaluation and Benchmarking0
Generating Synthetic Clinical Speech Data through Simulated ASR Deletion Error0
SSR7000: A Synchronized Corpus of Ultrasound Tongue Imaging for End-to-End Silent Speech RecognitionCode0
Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut0
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection0
Extracting Linguistic Knowledge from Speech: A Study of Stop Realization in 5 Romance Languages0
Evaluation of Off-the-shelf Speech Recognizers on Different Accents in a Dialogue Domain0
Building Open-source Speech Technology for Low-resource Minority Languages with SáMi as an Example – Tools, Methods and Experiments0
ParlaSpeech-HR - a Freely Available ASR Dataset for Croatian Bootstrapped from the ParlaMint Corpus0
ParlamentParla: A Speech Corpus of Catalan Parliamentary Sessions0
Samrómur: Crowd-sourcing large amounts of data0
Adversarial synthesis based data-augmentation for code-switched spoken language identification0
Speaker Identification using Speech Recognition0
Adaptive Activation Network For Low Resource Multilingual Speech Recognition0
Is Lip Region-of-Interest Sufficient for Lipreading?0
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning0
Contrastive Siamese Network for Semi-supervised Speech Recognition0
Acoustic-to-articulatory Speech Inversion with Multi-task Learning0
Contextual Adapters for Personalized Speech Recognition in Neural Transducers0
Clinical Dialogue Transcription Error Correction using Seq2Seq Models0
Joint Training of Speech Enhancement and Self-supervised Model for Noise-robust ASR0
Improving CTC-based ASR Models with Gated Interlayer Collaboration0
Heterogeneous Reservoir Computing Models for Persian Speech Recognition0
Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation0
Semantic-preserved Communication System for Highly Efficient Speech Transmission0
On Building Spoken Language Understanding Systems for Low Resourced Languages0
FLEURS: Few-shot Learning Evaluation of Universal Representations of SpeechCode0
An Investigation on Applying Acoustic Feature Conversion to ASR of Adult and Child Speech0
DPSNN: A Differentially Private Spiking Neural Network with Temporal Enhanced Pooling0
Adaptive multilingual speech recognition with pretrained models0
Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition0
Training Efficient CNNS: Tweaking the Nuts and Bolts of Neural Networks for Lighter, Faster and Robust ModelsCode0
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection0
Deep Learning for Visual Speech Analysis: A Survey0
Self-Supervised Speech Representation Learning: A Review0
Set-based Meta-Interpolation for Few-Task Meta-Learning0
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement0
Predicting electrode array impedance after one month from cochlear implantation surgery0
Automatic Spoken Language Identification using a Time-Delay Neural Network0
Content-Context Factorized Representations for Automated Speech Recognition0
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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